![Yuhua Li](https://profiles-cardiff.imgix.net/attachments/1592?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Yuhua Li
Reader of Machine Learning
School of Computer Science and Informatics
- LiY180@cardiff.ac.uk
- +44 29208 75317
- Abacws, Room 4.59, Senghennydd Road, Cathays, Cardiff, CF24 4AG
- Available for postgraduate supervision
Overview
Data Analytics and Machine Learning Research Group
I have conducted fundamental and applied research in machine learning, pattern recognition, data science, semantic similarity analysis and condition monitoring.
My experience in machine learning and pattern recognition includes statistical, geometrical methods and neural networks for feature/pattern selection and data analysis, knowledge discovery and inference.
My contribution to machine learning includes the development of anomaly/novelty detection methods for safety/mission-critical systems, which have limited or no data/knowledge on rare events, and informative observation selection techniques for sensors/measurements location optimisation for problems such as effective monitoring and process control. My works have motivated other researchers to develop new AI algorithms, use them as benchmarks and adopt them in products.
I have led and carried out research projects funded by the government and industry. I have collaborated on research projects with different sizes of national and international companies. My research has been applied to solve problems in digital manufacturing, condition monitoring, financial engineering, and other real-world problems.
External Engagement
- Member of the EPSRC Peer Review College
- Academic Adviser to the Commonwealth Scholarship Commission UK
- Member of the Innovation Advisory Council for Wales
- Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems
Publication
2023
- Wang, H., Treder, M., Marshall, D., Jones, D. and Li, Y. 2023. A skewed loss function for correcting predictive bias in brain age prediction. IEEE Transactions on Medical Imaging 42(6), pp. 1577-1589. (10.1109/TMI.2022.3231730)
- Zhang, F., Zhao, H., Li, Y., Wu, Y. and Sun, X. 2023. CBA-GAN: Cartoonization style transformation based on the convolutional attention module. Computers and Electrical Engineering 106, article number: 108575. (10.1016/j.compeleceng.2022.108575)
- Anggoro, A. W., Corcoran, P., De Widt, D. and Li, Y. 2023. Using DistilBERT to assign HS codes to international trading transactions. Presented at: World Conference on Information Systems and Technologies, Pisa, Italy, 4 - 6 April 2023.
2022
- Sidorowicz, T., Peres, P. and Li, Y. 2022. A novel approach for cross-selling insurance products using positive unlabelled learning. Presented at: International Joint Conference on Neural Networks, Padua - Italy, 18-23 July 20222022 International Joint Conference on Neural Networks (IJCNN). IEEE, (10.1109/IJCNN55064.2022.9892762)
- Bent, G., Simpkin, C., Li, Y. and Preece, A. 2022. Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations. Presented at: SPIE Defense + Commercial Sensing 2022, Orlando, Florida, United States, 3 April - 13 June 2022 Presented at Pham, T. and Solomon, L. eds.Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Vol. 12113. SPIE pp. 121131M., (10.1117/12.2617362)
- Bent, G., Simpkin, C., Li, Y. and Preece, A. 2022. Hyperdimensional computing using time-to-spike neuromorphic circuits. Presented at: 2022 IEEE World Congress on Computational Intelligence (WCCI), Padova, Italy, 18-23 July 2022. IEEE
2021
- Sohail, M., Peres, P. and Li, Y. 2021. Feature importance analysis for customer management of insurance products. Presented at: 2021 International Joint Conference on Neural Networks (IJCNN), Virtual, 18-22 July 2021.
2020
- Zhao, H., Zheng, J., Wang, Y., Yuan, X. and Li, Y. 2020. Portrait style transfer using deep convolutional neural networks and facial segmentation. Computers and Electrical Engineering 85, article number: 106655. (10.1016/j.compeleceng.2020.106655)
- Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L. P. and McGinnity, T. 2020. A review of learning in biologically plausible spiking neural networks. Neural Networks 122, pp. 253-272. (10.1016/j.neunet.2019.09.036)
2018
- Taherkhani, A., Belatreche, A., Li, Y. and Maguire, L. P. 2018. A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks. IEEE Transactions on Neural Networks and Learning Systems 29(11), pp. 5394-5407. (10.1109/TNNLS.2018.2797801)
- Liu, S. et al. 2018. Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach. Computers in Biology and Medicine 92, pp. 168-175. (10.1016/j.compbiomed.2017.11.014)
2017
- Shynkevich, Y., McGinnity, T., Coleman, S. A., Belatreche, A. and Li, Y. 2017. Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing 264, pp. 71--88. (10.1016/j.neucom.2016.11.095)
- Zhai, J., Cao, Y., Yao, Y., Ding, X. and Li, Y. 2017. Coarse and fine identification of collusive clique in financial market. Expert Systems with Applications 69, pp. 225-238. (10.1016/j.eswa.2016.10.051)
- Zhai, J., Cao, Y., Yao, Y., Ding, X. and Li, Y. 2017. Computational intelligent hybrid model for detecting disruptive trading activity. Decision Support Systems 93, pp. 26--41. (10.1016/j.dss.2016.09.003)
2016
- Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2016. Detecting wash trade in financial market using digraphs and dynamic programming. IEEE Transactions on Neural Networks and Learning Systems 27(11), pp. 2351-2363. (10.1109/TNNLS.2015.2480959)
- Raza, H., Cecotti, H., Li, Y. and Prasad, G. 2016. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface. Soft Computing 20(8), pp. 3085--3096. (10.1007/s00500-015-1937-5)
- Liu, J., Harkin, J., Li, Y. and Maguire, L. P. 2016. Fault-tolerant networks-on-chip routing with coarse and fine-grained look-ahead. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35(2), pp. 260-273. (10.1109/TCAD.2015.2459050)
2015
- Liu, J., Harkin, J., Li, Y. and Maguire, L. 2015. Low cost fault-tolerant routing algorithm for Networks-on-Chip. Microprocessors and Microsystems 39(6), pp. 358-372. (10.1016/j.micpro.2015.06.002)
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. P. 2015. Novelty detection using level set methods. IEEE Transactions on Neural Networks and Learning Systems 26(3), pp. 576-588. (10.1109/TNNLS.2014.2320293)
- Raza, H., Prasad, G. and Li, Y. 2015. EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition 48(3), pp. 659-669. (10.1016/j.patcog.2014.07.028)
- Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2015. Adaptive hidden Markov model with anomaly states for price manipulation detection. IEEE Transactions on Neural Networks and Learning Systems 26(2), pp. 318-330. (10.1109/TNNLS.2014.2315042)
- Taherkhani, A., Belatreche, A., Li, Y. and Maguire, L. P. 2015. DL-ReSuMe: A delay learning-based remote supervised method for spiking neurons. IEEE Transactions on Neural Networks and Learning Systems 26(12), pp. 3137-3149. (10.1109/TNNLS.2015.2404938)
2014
- Liu, J., Harkin, J., Li, Y., Maguire, L. and Linares-Barranco, A. 2014. Low overhead monitor mechanism for fault-tolerant analysis of NoC. Presented at: 8th International Symposium On Embedded Multicore/manycore Socs, Aizu-Wakamatsu, Japan, 23-25 Sep 20148th International Symposium on Embedded Multicore/manycore Socs. IEEE pp. 189-196., (10.1109/MCSoC.2014.35)
- Liu, J., Harkin, J., Li, Y. and Maguire, L. 2014. Online fault detection for Networks-on-Chip interconnect. Presented at: 2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), Leicester, UK, 14-17 Jul 20142014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2014). Piscataway, NJ: IEEE pp. 31-38., (10.1109/AHS.2014.6880155)
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. P. 2014. An experimental evaluation of novelty detection methods. Neurocomputing 135, pp. 313-327. (10.1016/j.neucom.2013.12.002)
- Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2014. A hidden Markov model with abnormal states for detecting stock price manipulation. Presented at: SMC 2013, Manchester, UK, 13-16 Oct 20132013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Piscataway, NJ: IEEE pp. 3014-3019., (10.1109/SMC.2013.514)
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. P. 2014. Novelty detection using level set methods with adaptive boundaries. Presented at: SMC 2013, Manchester, UK, 13-16 Oct 20132013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Piscataway, NJ: IEEE pp. 3020-3025., (10.1109/SMC.2013.515)
- Raza, H., Prasad, G. and Li, Y. 2014. Dataset shift detection in non-stationary environments using EWMA charts. Presented at: 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK, 13-16 Oct 20132013 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, New Jersey: IEEE pp. 3151-3156., (10.1109/SMC.2013.537)
- Liu, J., Harkin, J., Li, Y. and Maguire, L. 2014. Online traffic-aware fault detection for networks-on-chip. Journal of Parallel and Distributed Computing 74(1), pp. 1984-1993. (10.1016/j.jpdc.2013.09.001)
- McDonald, S., Coleman, S., McGinnity, T. M. and Li, Y. 2014. A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets. Presented at: 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX. USA, 4-9 August 2013The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE pp. 1-7., (10.1109/IJCNN.2013.6706965)
2013
- Goel, G., Maguire, L., Li, Y. and McLoone, S. 2013. Evaluation of sampling methods for learning from imbalanced data. Presented at: ICIC 2013, Nanning, China, 28-31 Jul 2013 Presented at Huang, D. et al. eds.Intelligent Computing Theories, Vol. 7995. Information Systems and Applications, incl. Internet/Web, and HCI Berlin, Heidelberg: Springer pp. 392-401., (10.1007/978-3-642-39479-9_47)
- Raza, H., Prasad, G. and Li, Y. 2013. EWMA based two-stage dataset shift-detection in non-stationary environments. Presented at: AIAI 2013, Paphos, Cyprus, 30 Sep - 2 Oct 2013 Presented at Papadopoulos, H. et al. eds.Artificial Intelligence Applications and Innovations, Vol. 412. IFIP Advances in Information and Communication Technology Berlin, Heidelberg: Springer pp. 625-635., (10.1007/978-3-642-41142-7_63)
2012
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. 2012. Constructing minimum volume surfaces using level set methods for novelty detection. Presented at: IJCNN 2012 International Join Conference on Neural Networks, Brisbane, QLD, Australia, 10-15 June 2012The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN) IEEE pp. 1-6., (10.1109/IJCNN.2012.6252804)
2011
- Zhang, K., Li, Y., Scarf, P. and Ball, A. 2011. Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks. Neurocomputing 74(17), pp. 2941-2952. (10.1016/j.neucom.2011.03.043)
- Zhang, K., Ball, A. D., Li, Y. and Gu, F. 2011. A novel feature selection algorithm for high-dimensional condition monitoring data. International Journal of Condition Monitoring 1(1), pp. 33-43. (10.1784/204764211798089075)
- Li, Y. and Maguire, L. 2011. Selecting critical patterns based on local geometrical and statistical information. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(6), pp. 1189-1201. (10.1109/TPAMI.2010.188)
- Li, Y. 2011. Selecting training points for one-class support vector machines. Pattern recognition letters 32(11), pp. 1517-1522. (10.1016/j.patrec.2011.04.013)
2009
- Li, Y. 2009. A surface representation approach for novelty detection. Presented at: International Conference on Information and Automation 2008, Changsha, China, 20-23 June 20082008 International Conference on Information and Automation. IEEE pp. 1464-1468., (10.1109/ICINFA.2008.4608233)
2007
- Zhang, K., Ball, A., Gu, F. and Li, Y. 2007. A hybrid model with a weighted voting scheme for feature selection in machinery condition monitoring. Presented at: IEEE International Conference on Automation Science and Engineering 2007, Scottsdale, AZ, United States, 22-25 September 20072007 IEEE International Conference on Automation Science and Engineering. IEEE pp. 174-179., (10.1109/COASE.2007.4341697)
2006
- Ben Sasi, A. Y., Gu, F., Li, Y. and Ball, A. D. 2006. A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed. Mechanical Systems and Signal Processing 20(7), pp. 1572-1589. (10.1016/j.ymssp.2005.09.010)
- Li, Y., McLean, D., Bandar, Z., O'Shea, J. and Crockett, K. 2006. Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering 18(8), pp. 1138-1150. (10.1109/TKDE.2006.130)
2005
- Li, Y., Gu, F., Harris, G., Ball, A., Bennett, N. and Travis, K. 2005. The measurement of instantaneous angular speed. Mechanical Systems and Signal Processing 19(4), pp. 786-805. (10.1016/j.ymssp.2004.04.003)
- Gu, F., Yesilyurt, I., Li, Y., Harris, G. and Ball, A. 2005. An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis. Mechanical Systems and Signal Processing 20(6), pp. 1444-1460. (10.1016/j.ymssp.2005.02.001)
2003
- Li, Y., Bandar, Z. and McLean, D. 2003. An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering 15(4), pp. 871-882. (10.1109/TKDE.2003.1209005)
2002
- Li, Y., Bandar, Z. and Mclean, D. 2002. Measuring semantic similarity between words using lexical knowledge and neural networks. Presented at: 3rd International Conference on Intelligent Data Engineering and Automated Learning — IDEAL 2002, Manchester, England, UK, 12-14 August 2002Intelligent Data Engineering and Automated Learning — IDEAL 2002, Vol. 2412. Lecture Notes in Computer Science Berlin and Heidelberg: Springer pp. 111-116., (10.1007/3-540-45675-9_19)
- Li, Y., Pont, M. J. and Jones, N. B. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where 'unknown' faults may occur. Pattern Recognition Letters 23(5), pp. 569-577. (10.1016/S0167-8655(01)00133-7)
- Li, Y., Pont, M. J. and Barrie Jones, N. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur. Pattern recognition letters 23(5), pp. 569-577. (10.1016/S0167-8655(01)00133-7)
2001
- Li, Y., Pont, M. J., Jones, N. B. and Twiddle, J. 2001. Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems. Transactions of the Institute of Measurement and Control 23(5), pp. 315-343. (10.1177/014233120102300504)
2000
- Li, Y., Pont, M. J., Parikh, C. R. and Jones, N. B. 2000. Comparing the performance of three neural classifiers for use in embedded applications. Presented at: Workshop 99 on Recent Advances in Soft Computing, Leicester, England, 01-02 July 1999 Presented at John, R. and Birkenhead, R. eds.Soft Computing Techniques and Applications. Advances in Soft Computing Physica pp. 34-29.
Articles
- Wang, H., Treder, M., Marshall, D., Jones, D. and Li, Y. 2023. A skewed loss function for correcting predictive bias in brain age prediction. IEEE Transactions on Medical Imaging 42(6), pp. 1577-1589. (10.1109/TMI.2022.3231730)
- Zhang, F., Zhao, H., Li, Y., Wu, Y. and Sun, X. 2023. CBA-GAN: Cartoonization style transformation based on the convolutional attention module. Computers and Electrical Engineering 106, article number: 108575. (10.1016/j.compeleceng.2022.108575)
- Zhao, H., Zheng, J., Wang, Y., Yuan, X. and Li, Y. 2020. Portrait style transfer using deep convolutional neural networks and facial segmentation. Computers and Electrical Engineering 85, article number: 106655. (10.1016/j.compeleceng.2020.106655)
- Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L. P. and McGinnity, T. 2020. A review of learning in biologically plausible spiking neural networks. Neural Networks 122, pp. 253-272. (10.1016/j.neunet.2019.09.036)
- Taherkhani, A., Belatreche, A., Li, Y. and Maguire, L. P. 2018. A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks. IEEE Transactions on Neural Networks and Learning Systems 29(11), pp. 5394-5407. (10.1109/TNNLS.2018.2797801)
- Liu, S. et al. 2018. Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach. Computers in Biology and Medicine 92, pp. 168-175. (10.1016/j.compbiomed.2017.11.014)
- Shynkevich, Y., McGinnity, T., Coleman, S. A., Belatreche, A. and Li, Y. 2017. Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing 264, pp. 71--88. (10.1016/j.neucom.2016.11.095)
- Zhai, J., Cao, Y., Yao, Y., Ding, X. and Li, Y. 2017. Coarse and fine identification of collusive clique in financial market. Expert Systems with Applications 69, pp. 225-238. (10.1016/j.eswa.2016.10.051)
- Zhai, J., Cao, Y., Yao, Y., Ding, X. and Li, Y. 2017. Computational intelligent hybrid model for detecting disruptive trading activity. Decision Support Systems 93, pp. 26--41. (10.1016/j.dss.2016.09.003)
- Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2016. Detecting wash trade in financial market using digraphs and dynamic programming. IEEE Transactions on Neural Networks and Learning Systems 27(11), pp. 2351-2363. (10.1109/TNNLS.2015.2480959)
- Raza, H., Cecotti, H., Li, Y. and Prasad, G. 2016. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface. Soft Computing 20(8), pp. 3085--3096. (10.1007/s00500-015-1937-5)
- Liu, J., Harkin, J., Li, Y. and Maguire, L. P. 2016. Fault-tolerant networks-on-chip routing with coarse and fine-grained look-ahead. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35(2), pp. 260-273. (10.1109/TCAD.2015.2459050)
- Liu, J., Harkin, J., Li, Y. and Maguire, L. 2015. Low cost fault-tolerant routing algorithm for Networks-on-Chip. Microprocessors and Microsystems 39(6), pp. 358-372. (10.1016/j.micpro.2015.06.002)
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. P. 2015. Novelty detection using level set methods. IEEE Transactions on Neural Networks and Learning Systems 26(3), pp. 576-588. (10.1109/TNNLS.2014.2320293)
- Raza, H., Prasad, G. and Li, Y. 2015. EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition 48(3), pp. 659-669. (10.1016/j.patcog.2014.07.028)
- Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2015. Adaptive hidden Markov model with anomaly states for price manipulation detection. IEEE Transactions on Neural Networks and Learning Systems 26(2), pp. 318-330. (10.1109/TNNLS.2014.2315042)
- Taherkhani, A., Belatreche, A., Li, Y. and Maguire, L. P. 2015. DL-ReSuMe: A delay learning-based remote supervised method for spiking neurons. IEEE Transactions on Neural Networks and Learning Systems 26(12), pp. 3137-3149. (10.1109/TNNLS.2015.2404938)
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. P. 2014. An experimental evaluation of novelty detection methods. Neurocomputing 135, pp. 313-327. (10.1016/j.neucom.2013.12.002)
- Liu, J., Harkin, J., Li, Y. and Maguire, L. 2014. Online traffic-aware fault detection for networks-on-chip. Journal of Parallel and Distributed Computing 74(1), pp. 1984-1993. (10.1016/j.jpdc.2013.09.001)
- Zhang, K., Li, Y., Scarf, P. and Ball, A. 2011. Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks. Neurocomputing 74(17), pp. 2941-2952. (10.1016/j.neucom.2011.03.043)
- Zhang, K., Ball, A. D., Li, Y. and Gu, F. 2011. A novel feature selection algorithm for high-dimensional condition monitoring data. International Journal of Condition Monitoring 1(1), pp. 33-43. (10.1784/204764211798089075)
- Li, Y. and Maguire, L. 2011. Selecting critical patterns based on local geometrical and statistical information. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(6), pp. 1189-1201. (10.1109/TPAMI.2010.188)
- Li, Y. 2011. Selecting training points for one-class support vector machines. Pattern recognition letters 32(11), pp. 1517-1522. (10.1016/j.patrec.2011.04.013)
- Ben Sasi, A. Y., Gu, F., Li, Y. and Ball, A. D. 2006. A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed. Mechanical Systems and Signal Processing 20(7), pp. 1572-1589. (10.1016/j.ymssp.2005.09.010)
- Li, Y., McLean, D., Bandar, Z., O'Shea, J. and Crockett, K. 2006. Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering 18(8), pp. 1138-1150. (10.1109/TKDE.2006.130)
- Li, Y., Gu, F., Harris, G., Ball, A., Bennett, N. and Travis, K. 2005. The measurement of instantaneous angular speed. Mechanical Systems and Signal Processing 19(4), pp. 786-805. (10.1016/j.ymssp.2004.04.003)
- Gu, F., Yesilyurt, I., Li, Y., Harris, G. and Ball, A. 2005. An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis. Mechanical Systems and Signal Processing 20(6), pp. 1444-1460. (10.1016/j.ymssp.2005.02.001)
- Li, Y., Bandar, Z. and McLean, D. 2003. An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering 15(4), pp. 871-882. (10.1109/TKDE.2003.1209005)
- Li, Y., Pont, M. J. and Jones, N. B. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where 'unknown' faults may occur. Pattern Recognition Letters 23(5), pp. 569-577. (10.1016/S0167-8655(01)00133-7)
- Li, Y., Pont, M. J. and Barrie Jones, N. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur. Pattern recognition letters 23(5), pp. 569-577. (10.1016/S0167-8655(01)00133-7)
- Li, Y., Pont, M. J., Jones, N. B. and Twiddle, J. 2001. Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems. Transactions of the Institute of Measurement and Control 23(5), pp. 315-343. (10.1177/014233120102300504)
Conferences
- Anggoro, A. W., Corcoran, P., De Widt, D. and Li, Y. 2023. Using DistilBERT to assign HS codes to international trading transactions. Presented at: World Conference on Information Systems and Technologies, Pisa, Italy, 4 - 6 April 2023.
- Sidorowicz, T., Peres, P. and Li, Y. 2022. A novel approach for cross-selling insurance products using positive unlabelled learning. Presented at: International Joint Conference on Neural Networks, Padua - Italy, 18-23 July 20222022 International Joint Conference on Neural Networks (IJCNN). IEEE, (10.1109/IJCNN55064.2022.9892762)
- Bent, G., Simpkin, C., Li, Y. and Preece, A. 2022. Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations. Presented at: SPIE Defense + Commercial Sensing 2022, Orlando, Florida, United States, 3 April - 13 June 2022 Presented at Pham, T. and Solomon, L. eds.Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Vol. 12113. SPIE pp. 121131M., (10.1117/12.2617362)
- Bent, G., Simpkin, C., Li, Y. and Preece, A. 2022. Hyperdimensional computing using time-to-spike neuromorphic circuits. Presented at: 2022 IEEE World Congress on Computational Intelligence (WCCI), Padova, Italy, 18-23 July 2022. IEEE
- Sohail, M., Peres, P. and Li, Y. 2021. Feature importance analysis for customer management of insurance products. Presented at: 2021 International Joint Conference on Neural Networks (IJCNN), Virtual, 18-22 July 2021.
- Liu, J., Harkin, J., Li, Y., Maguire, L. and Linares-Barranco, A. 2014. Low overhead monitor mechanism for fault-tolerant analysis of NoC. Presented at: 8th International Symposium On Embedded Multicore/manycore Socs, Aizu-Wakamatsu, Japan, 23-25 Sep 20148th International Symposium on Embedded Multicore/manycore Socs. IEEE pp. 189-196., (10.1109/MCSoC.2014.35)
- Liu, J., Harkin, J., Li, Y. and Maguire, L. 2014. Online fault detection for Networks-on-Chip interconnect. Presented at: 2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), Leicester, UK, 14-17 Jul 20142014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2014). Piscataway, NJ: IEEE pp. 31-38., (10.1109/AHS.2014.6880155)
- Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2014. A hidden Markov model with abnormal states for detecting stock price manipulation. Presented at: SMC 2013, Manchester, UK, 13-16 Oct 20132013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Piscataway, NJ: IEEE pp. 3014-3019., (10.1109/SMC.2013.514)
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. P. 2014. Novelty detection using level set methods with adaptive boundaries. Presented at: SMC 2013, Manchester, UK, 13-16 Oct 20132013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Piscataway, NJ: IEEE pp. 3020-3025., (10.1109/SMC.2013.515)
- Raza, H., Prasad, G. and Li, Y. 2014. Dataset shift detection in non-stationary environments using EWMA charts. Presented at: 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK, 13-16 Oct 20132013 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, New Jersey: IEEE pp. 3151-3156., (10.1109/SMC.2013.537)
- McDonald, S., Coleman, S., McGinnity, T. M. and Li, Y. 2014. A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets. Presented at: 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX. USA, 4-9 August 2013The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE pp. 1-7., (10.1109/IJCNN.2013.6706965)
- Goel, G., Maguire, L., Li, Y. and McLoone, S. 2013. Evaluation of sampling methods for learning from imbalanced data. Presented at: ICIC 2013, Nanning, China, 28-31 Jul 2013 Presented at Huang, D. et al. eds.Intelligent Computing Theories, Vol. 7995. Information Systems and Applications, incl. Internet/Web, and HCI Berlin, Heidelberg: Springer pp. 392-401., (10.1007/978-3-642-39479-9_47)
- Raza, H., Prasad, G. and Li, Y. 2013. EWMA based two-stage dataset shift-detection in non-stationary environments. Presented at: AIAI 2013, Paphos, Cyprus, 30 Sep - 2 Oct 2013 Presented at Papadopoulos, H. et al. eds.Artificial Intelligence Applications and Innovations, Vol. 412. IFIP Advances in Information and Communication Technology Berlin, Heidelberg: Springer pp. 625-635., (10.1007/978-3-642-41142-7_63)
- Ding, X., Li, Y., Belatreche, A. and Maguire, L. 2012. Constructing minimum volume surfaces using level set methods for novelty detection. Presented at: IJCNN 2012 International Join Conference on Neural Networks, Brisbane, QLD, Australia, 10-15 June 2012The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN) IEEE pp. 1-6., (10.1109/IJCNN.2012.6252804)
- Li, Y. 2009. A surface representation approach for novelty detection. Presented at: International Conference on Information and Automation 2008, Changsha, China, 20-23 June 20082008 International Conference on Information and Automation. IEEE pp. 1464-1468., (10.1109/ICINFA.2008.4608233)
- Zhang, K., Ball, A., Gu, F. and Li, Y. 2007. A hybrid model with a weighted voting scheme for feature selection in machinery condition monitoring. Presented at: IEEE International Conference on Automation Science and Engineering 2007, Scottsdale, AZ, United States, 22-25 September 20072007 IEEE International Conference on Automation Science and Engineering. IEEE pp. 174-179., (10.1109/COASE.2007.4341697)
- Li, Y., Bandar, Z. and Mclean, D. 2002. Measuring semantic similarity between words using lexical knowledge and neural networks. Presented at: 3rd International Conference on Intelligent Data Engineering and Automated Learning — IDEAL 2002, Manchester, England, UK, 12-14 August 2002Intelligent Data Engineering and Automated Learning — IDEAL 2002, Vol. 2412. Lecture Notes in Computer Science Berlin and Heidelberg: Springer pp. 111-116., (10.1007/3-540-45675-9_19)
- Li, Y., Pont, M. J., Parikh, C. R. and Jones, N. B. 2000. Comparing the performance of three neural classifiers for use in embedded applications. Presented at: Workshop 99 on Recent Advances in Soft Computing, Leicester, England, 01-02 July 1999 Presented at John, R. and Birkenhead, R. eds.Soft Computing Techniques and Applications. Advances in Soft Computing Physica pp. 34-29.
Research
Data Analytics and Machine Learning Research Group
My research interests include:
- Machine learning, pattern recognition
- Novelty detection, anomaly detection
- Data science, Big Data, text mining
- Neural networks, deep learning
- Hyperdimensional computing, vector symbolic architectures
- Condition monitoring and signal processing
- Machine learning and AI applications, e.g., cyber secuirity, finance, manufacturing
Selected publications (more publications on Google Scholar).
- Hanzhi Wang, Matthias Treder, Derek Jones, David Marshall, Yuhua Li (2023)
"A skewed loss function for correcting predictive bias in brain age prediction,"
IEEE Transactions on Medical Imaging, vol.42, no. 6, pp. 1577- 1589. - Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Liam Maguire (2018)
"A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks,"
IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5394 - 5407. - Yi Cao, Yuhua Li, Sonya Coleman, Ammar Belatreche, Martin McGinnity (2016)
"Detecting wash trade in financial market using digraphs and dynamic programming,"
IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 11, pp. 2351-2363. - Junxiu Liu, Jim Harkin, Yuhua Li, Liam Maguire (2016)
"Fault tolerant networks-on-chip routing with coarse and fine-grained look-ahead"
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, no. 2, pp. 260-273. - Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Liam Maguire (2015)
"DL-ReSuMe: A delay learning based remote supervised method for spiking neurons,"
IEEE Transactions on Neural Networks and Learning Systems , vol.26, no.12, pp. 3137- 3149. - Xuemei Ding, Yuhua Li, Ammar Belatreche, Liam Maguire (2015)
"Novelty detection using level set methods,"
IEEE Transactions on Neural Networks and Learning Systems. vol. 26, no. 3, pp. 576-588. - Yi Cao, Yuhua Li, Sonya Coleman, Ammar Belatreche, Martin McGinnity (2015)
"Adaptive hidden Markov model with abnormal states for price manipulation detection,"
IEEE Transactions on Neural Networks and Learning Systems, vol.26, no.2, pp. 318-330. - Haider Raza, Girijesh Prasad, Yuhua Li (2015)
"EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments,"
Pattern Recognition, vol. 48, no. 3, pp. 659-669. - Yuhua Li, Liam Maguire (2011)
"Selecting critical patterns based on local geometrical and statistical information,"
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1189-1201. - Yuhua Li (2011)
"Selecting training points for one-class support vector machines,"
Pattern Recognition Letters, vol. 32, no. 11, pp. 1517-1522. - Yuhua Li, David McLean, Zuhair Bandar, James O'Shea, Keeley Crockett. (2006)
"Sentence similarity using semantic nets and corpus statistics,"
IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1138-1150. - Yuhua Li, Zuhair Bandar, David McLean. (2003)
"An approach for measuring semantic similarity using multiple information sources,"
IEEE Transactions on Knowledge and Data Engineering, vol. 15, no.4, pp. 871-882. - Yuhua Li, Michael Pont, Barrie Jones (2002)
"Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where "unknown" faults may occur,"
Pattern Recognition Letters, vol.23, no.5, pp. 569-577.
Teaching
I received a postgraduate certificate in higher education, I am a Fellow of the HEA. To teach:
- CMT307 Applied Machine Learning
- CMT316 Applications of Machine Learning: Natural Language Processing/Computer Vision
- CMT219 Algorithms, Data Structures and Programming
- CM1210 Object Oriented Java Programming
Supervisions
Current PhD students
I am the 1st supervisor for:
- Angga Anggoro (with Dr Padraig Corcoran, Dr Dennis De Widt)
- Hanzhi Wang (with Prof Derek Jones, Prof David Marshall, Dr Matthias Treder)
- James Lewis-Cheetham (with Dr Federico Liberatore, Prof Qingwei Wang)
- Jianqiao Weng (with Dr Oktay Karakus)
- Mengqi Zhang (with Prof Krishna Singh, Prof David Marshall, Dr Matthias Treder)
- Nawal Alqurashi (with Dr Kirill Sidorov, Prof David Marshall)
I am interested in supervising PhD students in the areas of:
- Machine learning, pattern recognition
- Data science, Big Data, text mining
- Neural networks, deep learning
- Hyperdimensional computing, vector symbolic architectures
- Machine learning and AI applications, e.g., cyber secuirity, finance and engineering
You are welcome to contact me (LiY180@cardiff.ac.uk) if you have an outstanding academic background and high ambition for research excellence. Listed below are examples of PhD project proposals.
Project 1: Machine Learning for Health Monitoring of New-born Babies in Hospital Intensive Care Units
Hospital Neonatal Intensive Care Units (NICU) employ medical devices and sensors to continuously collect physiological data such as heart rate, temperature, and ECG. This project aims to harness the wealth of data available in NICU to develop effective solutions for neonate monitoring and early detection of diseases, with a particular focus on sepsis, which is responsible for 26% of neonate deaths. Utilizing machine learning techniques, the project will extract features from data streams, identify risk factors, and detect disease early to assist clinicians in providing better care for NICU patients. The project will be carried out in partnership with the School of Medicine, using NICU data collected from hospital wards.
Project 2: Transforming Machine Intelligence for Resource-Constrained Applications
In recent years, machine learning systems, including deep learning, have achieved remarkable success in various applications. However, they face significant challenges, such as high energy consumption, adapting to new conditions, and data deficiency, such as missing values, data imbalance, and data privacy. To address these challenges, this research harnesses the latest advancements in Vector Symbolic Architectures (VSA) or Hyperdimensional Computing (HDC), an emerging computing framework, to develop light-weight algorithms that can efficiently perform cognitive processing for resource-constrained applications at the network edge. VSA is inspired by the brain and represents and manipulates data in a high-dimensional vector space. Its holographic distributed representation and manipulation of information make computing more robust to noise, scalable, energy-efficient, and require less time and data for training and inference. This project aims to develop resource-efficient and data-frugal algorithms that can enhance and complement existing machine learning and deep learning methods.
Project 3: Online classification with emerging new classes
Standard classification methods can only classify pre-defined classes, i.e., they classify a new instance into one (or multiple) of the known classes. For example, for building a classifier for viral respiratory diseases, we need to train the classification model on a dataset with pre-defined classes such as MERS and SARS. At the time of developing a model for disease classification, the classifier is trained on available data which contains only, e.g., MERS and SARS. Such a classifier can only classify MERS and SARS diseases, it will be unable to deal with the emergence of new diseases such as COVID-19 in the future. To deal with the emergence of new classes, a novel approach is needed to learn a classifier that is able to detect newly emerging classes and adapt the classifier accordingly. Such a classifier learning paradigm with new classes has numerous applications, e.g., self-driving cars manoeuvring in novel traffic scenarios, malware detectors dealing with new types of network attacks, robotic soldiers navigating in new types of terrains, etc.
This project aims to develop a novel approach to learning a classifier that is capable of classifying emerging and novel classes. The proposed approach will address two main challenges: effective detection of emerging classes and just-in-time adaptation of classifiers for new classes. Emerging class detection will be built on the latest advances in novelty detection (novelty detection is a machine learning technique that learns a model based on only known classes to detect instances coming from a novel class), and just-in-time adaptation will develop a novel incremental learning strategy to integrate new classes into the current classifier. The developed algorithms will be evaluated on a use case in cybersecurity or the Internet of Things (e.g., new types of network attacks).
Project 4: Learning concept evolution in data streams
In applications with concept evolution, new concepts emerge in the data stream, and existing/known concepts disappear over time, e.g., new types of attacks in a computer network and new topics of interest in a social media data stream. This project aims to develop novel methods for tackling the challenging issue of concept evolution to enable the learned models to accommodate new concepts. It will achieve the following objectives: known concepts modelling; novel instances detection and accumulation; new concepts detection and integration; outdated concepts retiring.
Project 5: Explainable machine learning for securing Internet of Things (IoT)
Internet of Things (IoT) consists of things, services, and networks, it connects interrelated smart devices, objects, animals or people to transfer data over a network to serve people better. The amount of data transferred with IoT systems is continuous, heterogenous and huge, which makes IoT systems more vulnerable than the traditional network to malicious activities from attackers, so the security and privacy of this highly automated network is a key challenge for the deployment of Internet of Things (IoT). It is constantly subject to adversarial attacks including denial of service, jamming, spoofing, eavesdropping, malware and privacy leakage. The limited resources (computation, battery, and memory) on IoT devices and the amount of data generated and communicated severely constrain the applicability of existing security measures to IoT systems. Even if a security system is effective at the time of deployment, it is prone to fail soon as attackers adapt smarter strategies to foil the system and avoid detection. Machine learning is a major tool for detecting adversarial attacks, and human-level explainability of detection results remains open to research in the security of IoT.
This project aims to address these key challenges to secure future IoT systems with creative machine learning methods by Investigating data streaming classification methods for effectively detecting known types of attacks and their variants in the future; Developing computationally cheaper machine learning algorithms as well as robustness against eavesdropping attacks; Optimising the offloading policy in dynamic radio environments to optimally distribute the computational load over cloud, device and edge; Investigating adversarial machine learning techniques to tackle attackers' changing strategies; Interpreting prediction results to support human to take trustworthy actions.
