Dr Shaoning Pang

Professor
Computer Science
Location: Building 183, Room 3048
Email Dr Shaoning Pang

Professional memberships

Senior Member of Institute of Electrical and Electronics Engineers, IEEE

Profile

Dr. Paul Pang obtained his Ph.D. in intelligent systems, from Shanghai Jiao Tong University, China (2000). In 2003, he completed his postdoctoral research at Pohang University of Science and Technology (POSTECH), South Korea. From July 2003 to March 2011, he was a senior research fellow and research centre director at Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand.

He is currently a senior lecturer in the Department of Computing, Unitec Institute of Technology, New Zealand.

Research Interests

Dr Pang's research interests include Agent Data Mining, Incremental and Multi-task Learning, SVM Aggregating Intelligence, Intelligent Systems and Industry Applications.

He is a principal researcher in the Decentralized Machine Learning Intelligence Laboratory (DMLI).

Teaching
  • Data Mining and its applications to business and industry
  • Research and professional practice at Doctorate Level
  • Thesis supervision at masters and doctorate Level

Supervised thesis projects

Dacey, Simon (2017). Computational land use management of public spaces in New Zealand
Zhao, Jane (2016). Computer monitoring trends in recreational fishing effort
You, Li (2014). A multi-agent integrated analysis engine for decentralized network traffic monitoring
Song, Lei (2014). Spatio-temporal incremental data modelling for multidimensional environmental analysis
Martin, Bernd H. (2013). A methodology for multilevel analysis of scientific collaboration networks : mapping current computer science research in New Zealand
Tirumala, Sreenivas Sremath (2013). A quantum inspired competitive coevolution evolutionary algorithm
Veerisetty, Neeharika (2013). Load balancing in a distributed network environment : an ant colony inspired approach
Yip, Kai Leung (2012). Determining the accuracy of budgets : a machine learning application for budget change pattern recognition
Lei Zhu (2012). Dynamic class imbalance learning for incremental LPSVM
Zhang, Bing Qian (2011). An application of soft systems methodology on a holistic level: Recommendations for developing and implementing green ICT strategies in New Zealand

Publications

Pang, S., Zhao, J., Hartill, B., & Sarrafzadeh, A. (2016). Modelling land water composition scene for maritime traffic surveillance. International Journal of Applied Pattern Recognition (Vol. 3(4)).

Pang, S., Komosny, L. Z., Zhu, L., Zhang, R., Sarrafzadeh, A., Ban, T., & Inoue, D. (2016). Malicious Events Grouping via behaviour Based Darknet Traffic Flow Analysis. Wireless Personal Communications (Vol. 94(336)).

Ryan, K., Ko, L., Russello, G., Nelson, R., Pang, S., Cheang, A., Dobbie, G., Sarrafzadeh, A., Chaisiri, S., Rizwan, M. R., & Holmes, G. (2015). STRATUS: Towards Returning Data Control to Cloud Users. Wang, G., Zomaya, A., Perez, G. M & Li, K. ICA3PP International Workshops and Symposiums, China (Vol. 9532).

Zhang, P., Zhu, L., Xiaosong, L., Pang, S., Sarrafzadeh, A., & Komosny, D. (2015). Behavior Based Darknet Traffic Decomposition for Malicious Events identification. S. Arik et al. 22nd International Conference, ICONIP 2015, Istanbul, Turkey.

Chen, G., Douch, C., Zhang, M., & Pang, S. (2015). Reinforcement Learning in Continuous Spaces by Using Learning Fuzzy Classifier Systems. S. Arik et al, 22nd International Conference, ICONIP 2015, Istanbul, Turkey.

Zhao, J., Pang, S., Hartill, B., & Sarrafzadeh, A. (2015). Adaptive Background Modeling for Land and Water Composition Scenes. Murino, V., Puppo, E., & Vernazza, G. ICIAP2015.
http://hdl.handle.net/10652/3359

Chen, G., Zhang, M., Pang, S., & Douch, C. (2014). Stochastic Decision Making in Learning Classifier Systems through a Natural Policy Gradient Method. Lecture Notes in Computer Science, Neural Information Processing (Vol. 8836).

Tirumala, S. S., Chen, G., & Pang, S. (2014). Quantum Inspired Evolutionary Algorithm by Representing Candidate Solution as Normal Distribution. Lecture Notes in Computer Science, Neural Information Processing (Vol. 8836).

Dacey, S., Song, L., Zhu, L., & Pang, S. (2014). Analysis and Configuration of Boundary Difference Calculations. Lecture Notes in Computer Science, Neural Information Processing (Vol. 8836).

Song, L., Pang, S., Longley, I., Olivares, G., and Sarrafzadeh, A. (2014). Spatio-temporal PM 2.5 prediction by spatial data aided incremental support vector regression. Neural Networks (IJCNN), 2014 International Joint Conference, XX.

Pang, S., Liu, F., Kadobayashi, Y., Ban, T., & Inoue, D. (2014). A Learner-Independent Knowledge Transfer Approach to Multi-task Learning. Cognitive Computation (Vol. 6(3)).

Dacey, S., Song, L., and Pang, S. (2013). An Intelligent Agent Based Land Encroachment Detection Approach. Lecture Notes in Computer Science (Vol. 8226).

Ban, T., Zhang, R., Pang, S., Sarrafzadeh, A., and Inoue, A. (2013). Referential kNN Regression for Financial Time Series Forecasting. Lecture Notes in Computer Science (Vol. 8226).

Narayanan, A., Chen, Y., Pang, S., and Ban, T. (2013). The Effects of Different Representations on Static Structure Analysis of Computer Malware Signatures. The Scientific World Journal (Vol. 2013).

Chen, G., Sarrafzadeh, A., and Pang, S. (2013). Service Provision Control in Federated Service Providing Systems. IEEE Transactions on Parallel and Distributed Systems (Vol. 24/3).

Pang, S., Zhu, L., Chen, G., Sarrafzadeh , A., Ban, T., and Inoue , D. (2013). Dynamic class imbalance learning for incremental LPSVM. Neural Networks (Vol. 44).

Lai, A., Song, L., Peng, Y., Zhang, P., Wang, Q., and Pang, S. (2012). Exploring Crude Oil Impacts to Oil Stocks through Graphical Computational Correlation Analysis. Lecture Notes in Computer Science (Vol. 7667).

Zhu, L., Pang, S., Chen, G., and Sarrafzadeh, A. (2012). Class Imbalance Robust Incremental LPSVM for Data Streams Learning. International Joint Conference on Neural Networks (IJCNN2012), Brisbane, Australia.

Chen , Y., Narayanan, A., Pang, S., and Ban, T. (2012). Malicioius Software Detection Using Multiple Sequence Alignment and Data Mining. The 26th IEEE International Conference on Advanced Information Networking and Applications (AINA).

Ban, T., Zhu, L., Shimamura, J., Pang, S., and Inoue, D. (2012). Behavior Analysis of Long-term Cyber Attacks in the Darknet. International Conference on Neural Information Processing (ICONIP (5)).

Pang, S., Liu, F., Kadobayashi, Y., Ban, T., and Inoue, D. (2012). Training Minimum Enclosing Balls for Cross Tasks Knowledge Transfer. International Conference on Neural Information Processing (ICONIP).

Chen, Y., Narayanan, A., Pang, S., and Ban, T. (2012). Multiple sequence alignment and artificial neural networks for malicious software detection. Proc. of 2012 Eighth International Conference on Natural Computation (ICNC), Digital Object Identifier: 10.1109/ICNC.2012.6234576.

Narayanan, A., Chen, Y., Pang, S., and Ban, T. (2012). The Effects of Different Representations on Malware Motif Identification. Proc. of Eighth International Conference on Computational Intelligence and Security (CIS).

Chen, G., Pang, S., Sarrafzadeh, A., Ban, T., and Inoue, D. (2012). SDE-Driven Service Provision Control. Lecture Notes in Computer Science (Vol. 7663).

Pang, S., Ban, T., Kadobayashi, Y., and Kasabov, N. (2011). LDA Merging and Splitting With Applications to Multiagent Cooperative Learning and System Alteration. IEEE Transactions on System, Man, and Cybernetics-Part B (Vol. PP/99).

Pang, S., Ban, T., Kadobayashi, Y., and Kasabov, N. (2011). Personalized mode transductive spanning SVM classification tree. Information Science (Vol. 181/11).

S., Pang, T., Ban, Y., Kadobayashi, and N. K., Kasabov (2011). LDA Merging and Splitting With Applications to Multiagent Cooperative Learning and System Alteration. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on (Vol. 99).

S., Pang, Tao, Ban, Youki, Kadobayashi, and N., Kasabov (2011). Personalized mode transductive spanning SVM classification tree. Information Sciences (Vol. 181/11).

Osawa, Z., Pang, S., and Kasabov, N. (2011). Incremental Learning in Intelligent Systems: Theory and Applications, a monograph book, contracted with John Wiley & Sons, USA. John Wiley & Sons, USA.

Rafiq, Mohammed, and S., Pang (2011). Agent Personalized Call Center Traffic Prediction and Call Distribution. Neural Information Processing, 978-3-642-24957-0 (Vol. 7063).

Lu, B., He, Z., Yang, J., and Pang, S. (2010). A new FCM algorithm based on monkey-king genetic algorithm for remote sensing image segmengtation. Laser Journal, CNKI:SUN:JGZZ.0.2010-06-009.

Shimo, N., Pang, S., Horio, K., Kasabov, N., Tamukoh, H., Koga, T., Sonoh, S., Isogai, H., Yamakawa, T., Hanazawa, A., Miki, T., and Horio, K. (2010). Effective and Adaptive Learning Based on Diversive/Specific Curiosity. Brain-Inspired Information Technology, Isbn: 978-3-642-04024-5 (Vol. Volume 266).

Ozawa, S., Pang, S., and Kasabov Kasabov , N. (2010). Online Feature Extraction for Evolving Intelligent Systems. Evolving Intelligent Systems: Methodology and Applications, ISBN 978-0-470-28719-4.

Pang, S., Chen, G., Lei, S., and Kasabov, N. (2010). High Speed Algorithms for Outlier Detection and Classification over Huge-size Network Data Streams. Technical Project Report to National Institute of Information and Communication Technologies (NICT), Japan. Knowledge Engineering Discovery Research Institute, Auckland University of Technology.

Ozawa, S., Pang, S., and Kasabov, N. (2010). Online Feature Extraction for Evolving Intelligent Systems. P. Angelov, D. P. Filev and N. Kasabov (Eds). Evolving Intelligent Systems, John Wiley & Sons, Inc., Hoboken, JK, USA. doi: 10.1002/9780470569962.ch7.

Pang, S., Song, L., and Kasabov, N. (2010). Correlation-aided support vector regression for forex time series prediction. Neural Computing & Applications, DOI: 10.1007/s00521-010-0482-5.

Pang, S., and Ban, T. (2009). Adaptive Soft Computing Techniques and Applications. Special issue of Memetic Computing, ISSN: 1865-9292 (Vol. Volume 1).

Pang, S., and Kasabov, N. (2009). Encoding and decoding the knowledge of association rules over SVM classification trees. Knowledge and Information Systems (Vol. 19/1).

Pang, S., and Ban, T. (2009). Guest editorial: Thematic issue on 'Adaptive Soft Computing Techniques and Applications'. Memetic Computing (Vol. 1/4).

Gordon, S., Pang, S., Nishioka, R., Kasabov, N., and Yamakawa, T. (2009). Vision based mobile robot for indoor environmental security. ADVANCES IN NEURO-INFORMATION PROCESSING Lecture Notes in Computer Science, DOI: 10.1007/978-3-642-02490-0_117 (Vol. 5506/2009).

Pang, S., Dhoble , K., Chen, Y., Kasabov, N., Ban, T., and Kadobayashi, Y. (2009). Active Mode Incremental Nonparametric Discriminant Analysis Learning. Proc. of the Eighth International Conference on Information and Management Sciences.

Chen, Y., Pang, S., Kasabov, N., Ban, T., and Kadobayashi, Y. (2009). Hierarchical Core Vector Machines for Network Intrusion Detection. Proc. of ICONIP 2009, Part II (Vol. Volume 5864).

Michlovsky, Z., Pang, S., Kasabov, N., Ban, T., and Kadobayashi, Y. (2009). String kernel based SVM for internet security implementation. NEURAL INFORMATION PROCESSING Lecture Notes in Computer Science (Vol. 5864/2009).

Pang, S., Ban, T., Kadobayashi , Y., and Kasabov, N. (2009). Spanning SVM Tree for Personalized Transductive Learning. Proc. of ICANN 2009, Part I (Vol. Volume 5768).

Pang, S., Ozawa, S., and Kasabov, N. (2009). Curiosity Driven Incremental LDA Agent Active Learning. Proc. Of 2009 International Joint Conference on Neural Networks, Atlanta GA.

Pang, S., Ban, T., Kadobayashi, Y., and Kasabov, N. (2008). gSVMT: Aggregating SVMs over a Dynamic Grid Learned from Data. Proc. of 11th International Conference on Computer and Information Technology.

Pang, S., and Kasabov, N. (2008). r-SVMT: Discovering the Knowledge of Association Rule over SVM Classification Trees. Proc. of IEEE International Joint Conference on Neural Networks.

Ozawa, S., Matsumoto, K., Pang, S., and Kasabov, N. (2008). An Incremental Principal Component Analysis based on Dynamic Accumulation Ratio. SICE Annual Conference.

Ozawa, S., Pang, S., and Kasabov, N. (2008). Incremental Learning of Chunk Data for On-line Pattern Classification Systems. IEEE Transactions on Neural Networks (Vol. 19/6).

Ozawa, S., Pang, Shaoning, and Kasabov, N. (2008). Incremental Learning of Chunk Data for Online Pattern Classification Systems. Neural Networks, IEEE Transactions on (Vol. 19/6).

Shimo, N., Pang, S., Kasabov, N., and Yamakawa, T. (2008). Curiosity-Driven Multi-Agent Competitive and Cooperative LDA Learning. International Journal of Innovative Computing, Information and Control (Vol. 4/7).

Pang, S., and Kasabov, N. (2008). SVMT-rule: Association Rule Mining over SVM Classification Trees. Rule Extraction from Support Vector Machines, ISBN: 978-3-540-75389-6 (Vol. Volume 80).

Pang, S., and Kasabov, N. (2008). Wireless Network Coverage and Capacity Dynamic Optimization for NewZealand Telecom. Technical Project Report to Telecom New Zealand Ltd., Lucentand Bell Lab, and Foundation for Research Science & Technology (FRST) NewZealand, Knowledge Engineering Discovery Research Institute, Auckland Universityof Technology.

Pang, S., Havukkala, I., Hu, Y., and Kasabov, N. (2008). Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems. In Y.-Q. Zhang and J. C. Rajapakse (Eds.). Machine Learning in Bioinformatics, Hoboken, NJ: John Wiley & Sons.

Pang, S., and Chen, G. (2007). Speech Feature Extraction with reference to Smoking Quitting Voice Change Detection. Technical Project Report to Health Research Council (HRC) New Zealand, Knowledge Engineering Discovery Research Institute, Auckland University of Technology.

Pang, S., Havukkala, I., Hu, Y., and Kasabov, N. (2007). Classification Consistency Analysis for Bootstrapping Gene Selection. Neural Computing and Applications (Vol. 16/6).

Hu, Y., Pang, S., and Havukkala, I. (2006). A Novel Microarray Gene Selection Method Based on Consistency. Sixth International Conference on Hybrid Intelligent Systems, 2006. HIS '06.

Pang, S., and Kasabov, N. (2006). Investigating LLE Eigenface on Pose and Face Identification. ADVANCES IN NEURAL NETWORKS - ISNN 2006 Lecture Notes in Computer Science, DOI: 10.1007/11760023_21 (Vol. 3972/2006).

Havukkala, I., Benuskova, L., Pang, S., Jain, V., Kroon , R., and Kasabov, N. (2006). Image and Fractal Information Processing for Large-Scale Chemoinformatics, Genomics Analyses and Pattern Discovery. Proceedings of PRIB.

Ozawa, S., Pang, S., and Kasabov, N. (2006). On-line Feature Selection for Adaptive Evolving Connectionist Systems. International Journal of Innovative Computing, Information and Control (Vol. 10/10).

Ozawa, S., Pang, S., and Kasabov, N. (2006). Incremental Learning of Feature Space and Classifier for Online Pattern Recognition. Computer Science, Artificial Intelligence and Software Development (Vol. 10/1).

Pang, S., and Havukkala, I. (2006). Proceeding of 1st Korean New Zealand Joint Workshop on Advance of Computational Intelligence Methods and Applications. Auckland University of Technology, New Zealand.

Pang, S., Havukkala , I., and Kasabov, N. (2006). Two-Class SVM Trees (2-SVMT) for Biomarker Data Analysis. In J. Wang et al. (Eds.). ISNN 2006: Proceedings of International Symposium on Neural Networks, Berlin: Springer Verlag.

Kasabov, N., Zhang, D., and Pang, S. (2005). Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition. Advanced Robotics and its Social Impacts, 2005. IEEE Workshop on.

Pang, Shaoning, Ozawa, S., and Kasabov, N. (2005). Incremental linear discriminant analysis for classification of data streams. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on (Vol. 35/5).

Ozawa, S., Toh, S.L., Abe, S., Pang, Shaoning, and Kasabov, N. (2005). Incremental learning for online face recognition. Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on (Vol. 5/5).

Kasabov, N., and Pang, Shaoning (2003). Transductive support vector machines and applications in bioinformatics for promoter recognition. Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on (Vol. 1).