Huanhuan Chen – Publications

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Journal Papers

• Yang Li, Bingbing Jiang, Huanhuan Chen, and Xin Yao. "Symbolic Sequence Classification in the Fractal Space." IEEE Transactions on Emerging Topics in Computational Intelligence, Accepted.


• Kui Yu, Lin Liu, Jiuyong Li, and Huanhuan Chen "Mining Markov Blankets without Causal Sufficiency." IEEE Transactions on Neural Networks and Learning Systems, Accepted.


• Yaqiang Yao, Yan Liu, Zhenyu Liu Huanhuan Chen, Anthony Cohn. "Human Activity Recognition with Posture Tendency Descriptors on Action Snippets." IEEE Transactions on Big Data, 10.1109/TBDATA.2018.2803838.


• Bingbing Jiang, Zhengyu Li, Huanhuan Chen, Anthony Cohn. "Latent Topic Text Representation Learning on Statistical Manifolds." IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2018.2808332.


• Rodrigo Soares, Huanhuan Chen, Xin Yao. "Efficient Cluster-based Boosting for Semi-supervised Classification." IEEE Transactions on Neural Networks and Learning Systems, 10.1109/TNNLS.2018.2809623.


• Xiren Zhou, Huanhuan Chen, Jinlong Li, "An Automatic GPR B-scan Image Interpreting Model." IEEE Transactions on Geoscience and Remote Sensing, Accepted on January 23 2018.


• Zhichen Gong, Huanhuan Chen, Bo Yuan, and Xin Yao, "Multiobjective Learning in the Model Space for Time Series Classification." IEEE Transactions on Cybernetics, Accepted on December 30 2017. DOI: 10.1109/TCYB.2018.2789422.


• Huanhuan Chen, Bingbing Jiang, and Xin Yao, "Semi-supervised Negative Correlation Learning." IEEE Transactions on Neural Networks and Learning Systems, Accepted on December 2017. DOI: 10.1109/TNNLS.2017.2784814.


• Zhichen Gong, Huanhuan Chen, "Sequential Data Classification by Dynamic State Warping." Knowledge and Information Systems, DOI: https://doi.org/10.1007/s10115-017-1139-9.


• Bingbing Jiang, Huanhuan Chen, Bo Yuan, Xin Yao, "Scalable Graph-Based Semi-Supervised Learning Through Sparse Bayesian Model." IEEE Transactions on Knowledge and Data Engineering, 29(12):2758 - 2771, 2017.


• Rodrigo G. F. Soares, Huanhuan Chen, Xin Yao, "A Cluster-based Semi-supervised Ensemble for Multi-class Classification." IEEE Transactions on Emerging Topics in Computational Intelligence, 1(6):408 - 420, 2017.


• Bo Yuan, Huanhuan Chen, Xin Yao, "Optimal Relay Placement for Lifetime Maximization in Wireless Underground Sensor Networks." Information Sciences, vol. 418-419, pp. 463-479, 2017.


• Ke Liu, Dunwei Gong, Fanlin Meng, Huanhuan Chen, Gai-Ge Wang, "Gesture segmentation based on a two-phase estimation of distribution algorithm." Information Sciences, vol. 394, pp. 88-105, 2017.


• Xindong Wu, Huanhuan Chen, Jun Liu, Gong-Qing Wu, Ruqian Lu, Nanning Zheng, "Knowledge Engineering With Big Data (BigKE): A 54-Month, 45-Million RMB, 15-Institution National Grand Project." IEEE Access, vol. 5, pp. 12696-12701, 2017.


• Wei Fang, Jun Sun, Huanhuan Chen, Xiaojun Wu. "A decentralized quantum-inspired particle swarm optimization algorithm with cellular structured population." Information Sciences, 330:19–48, 2016.


• Xindong Wu, Huanhuan Chen, Gong-Qing Wu, etc. "Knowledge Engineering with Big Data." IEEE Intelligent Systems, 30(5):46-55, 2015.


• FengZhen Tang, Peter Tino, Pedro A.G. Pena, Huanhuan Chen. "The Benefits of Modelling Slack Variables in SVMs." Neural Computation, 27(4):954-81, 2015.


• Shan He, Huanhuan Chen, Zexuan Zhu, etc. "Robust Twin Boosting for Feature Selection from High-dimensional Omics Data with Label Noise." Information Science, 291:1–18, 2015.


• Bo Yuan,Bin Li,Huanhuan Chen,Xin Yao."A New Evolutionary Algorithm with Structure Mutation for the Maximum Balanced Biclique Problem."IEEE Transactions on Cybernetics, 45(5):1040-1053, 2015.


• Huanhuan Chen, Peter Tiňo, Xin Yao. "Cognitive Fault Diagnosis in Tennessee Eastman Process using Learning in the Model Space." Computers & Chemical Engineering, 2014. Available online or [here]


• Huanhuan Chen, Peter Tino, Ali Rodan, Xin Yao. "Learning in the Model Space for Cognitive Fault Diagnosis." IEEE Transactions on Neural Networks and Learning Systems, 25(1):124-136, 2014. Available [here]


• Huanhuan Chen, Peter Tino, Xin Yao. "Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection." IEEE Transactions on Neural Networks and Learning Systems, 25(2):356-369, 2014. Available [here]


• Huanhuan Chen, Xin Yao. "Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning." IEEE Transactions Knowledge Data Engineering, 22(12):1738-1751, 2010. Available [here]


• Huanhuan Chen, Peter Tiño, Xin Yao. "Predictive Ensemble Pruning by Expectation Propagation." IEEE Transactions Knowledge Data Engineering, 21(7): 999-1013, 2009. Available [here]


• Huanhuan Chen, Peter Tino, Xin Yao. "Probabilistic Classification Vector Machines." IEEE Transactions on Neural Networks , 20(6): 901-914 (2009).
  (IEEE Transactions on Neural Networks Outstanding 2009 Paper Award (bestowed in 2011)). Available [here]
  Matlab software Availablehere


• Huanhuan Chen, Xin Yao. "Regularized Negative Correlation Learning for Neural Network Ensembles." IEEE Transactions on Neural Networks, 20(12):1962-1979, 2009. Available [here]


• Joseba Quevedo, Huanhuan Chen, Miquel A. Cuguero, Peter Tino, Vicenc Puig, D. Garcia, R. Sarrate and Xin Yao. "Combining Learning in Model Space Fault Diagnosis with Data Validation/Reconstruction: Application to the Barcelona Water Network." Engineering Applications of Artificial Intelligence , 30: 18-29, (2014). Available [here]


• Rodrigo G. F. Soares, Huanhuan Chen, Xin Yao. "Semisupervised Classification With Cluster Regularization." IEEE Transactions Neural Networks and Learning Systems , 23(11): 1779-1792 (2012). Available [here]
  Matlab software Availablehere


• Lean Yu, Huanhuan Chen, Shouyang Wang, Kin Keung Lai. "Evolving Least Squares Support Vector Machines for Stock Market Trend Mining." IEEE Transactions Evolutionary Computation, 13(1): 87-102 (2009). Available [here]


• Qi Guo, Tianshi Chen, Yunji Chen, Rui Wang, Huanhuan Chen, Weiwu Hu, Guoliang Chen. "Pre-Silicon Bug Forecast." IEEE Transactions on CAD of Integrated Circuits and Systems, 33(3): 451-463 (2014). Available [here]


• Alex Royal, P.R. Atkins, Michael Brennan, David Chapman, Huanhuan Chen, Anthony Cohn, K Foo, Kevin Goddard, Russell Hayes, Tong Hao, P. L. Lewin, Nicole Metje, Jen Muggleton, Adham Naji, Giovanni Orlando, Steve Pennock, Miles Redfern, Adrian Saul, Steve Swingler, Ping Wang and C.D.F. Rogers. "Site Assessment of Multiple Sensor Approaches for Buried Utility Detection." International Journal of Geophysics (IJGE'11), Volume 2011, Article ID 496123, 19 pages, (2011). http://dx.doi.org/10.1155/2011/496123 Available [here]

Book Chapter

• Huanhuan Chen, Anthony Cohn and Xin Yao. Ensemble Learning by Negative Correlation Learning.In Ensemble Learning: Theory and Application, Zhang Cha and Yunqian Ma (Ed.). DOI 10.1007/978-1-4419-9326-7_6, Springer-Verlag, 2012.


• Arjun Chandra, Huanhuan Chen and Xin Yao Trade-off between Diversity and Accuracy in Ensemble Generation. In Multi-objective Machine Learning, Yaochu Jin (Ed.), pp.429-464, DOI 10.1007/3-540-33019-4_19 Springer-Verlag, 2006.

Conference Papers

• Junyuan Hong, Huanhuan Chen, Feng Lin. "Disturbance Grassmann Kernels for Subspace-Based Learning". In Proceedings of the ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD'18). , London, UK, August 19-23, 2018


• Yang Li, Junyuan Hong, Huanhuan Chen. "Sequential Data Classification in the Space of Liquid State Machines". In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML'16), Riva del Garda, Italy, 2016


• Jiawei Liu, Zhenyu Liu, Huanhuan Chen. "Revisit Word Embeddings with Semantic Lexicons for Modeling Lexical Contrast". In Proceedings of International Conference on Big Knowledge (ICBK'17), Hefei, China, 2017


• Dandan Yang, Huanhuan Chen, Yinlong Song, Zhichen Gong. "Granger Causality for Multivariate Time Series Classification". In Proceedings of International Conference on Big Knowledge (ICBK'17), Hefei, China, 2017


• Zicheng Gong, Huanhuan Chen. "Model-Based Oversampling for Imbalanced Sequence Classification". In Proceedings of The 25th ACM International Conference on Information and Knowledge Management (CIKM'16), Indianapolis, USA, 2016


• Huanhuan Chen, FengZhen Tang, Peter Tino, Anthony G Cohn, Xin Yao. "Model Metric Co-learning for Time Series Classification" In Proceedings of the 25th International Joint Conferences on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015


• Fengzhen Tang, Peter Tiño, Huanhuan Chen."Learning the deterministically constructed Echo State Networks." In Proeedings of the 2014 International Joint Conference on Neural Networks (IJCNN'14),2014: 77-83


• Fengzhen Tang, Peter Tiño, Pedro Antonio Gutiérrez, Huanhuan Chen."Support Vector Ordinal Regression using Privileged Information." ESANN 2014


• Huanhuan Chen, Fengzhen Tang, Peter Tino, Xin Yao. "Model-based kernel for efficient time series analysis." In Proceedings of the 19th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD'13). Chicago, USA, August 11-14, 2013. Available [here]


• Huanhuan Chen, Anthony G. Cohn. "Buried Utility Pipeline Mapping Based on Multiple Spatial Data Sources: A Bayesian Data Fusion Approach." In Proceedings of the 22nd International Joint Conferences on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011. Available [here]. Oral presentation and Poster presentation.


• Huanhuan Chen, Anthony G. Cohn: Buried Utility Pipeline Mapping based on Street Survey and Ground Penetrating Radar. In Proceedings of the 19th European Conference on Artificial Intelligence (ECAI'10) 2010:987-988, Lisbon, Portugal, 2010. Available [here]


• Huanhuan Chen, and Anthony G. Cohn. "Probabilistic conic mixture model and its applications to mining spatial ground penetrating radar data." In Workshop in SIAM Conference on Data Mining (WSDM'10), Columbus, 2010. Available [here]


• Huanhuan Chen, Anthony G. Cohn. "Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data." In Proceedings of the 2010 IEEE World Congress on Computational intelligence (WCCI'10), Barcelona, 2010. Available [here]


• Shuo Wang, Huanhuan Chen, Xin Yao. "Negative correlation learning for classification ensembles." In Proeedings of the 2010 International Joint Conference on Neural Networks (IJCNN,10), Barcelona, 2010. Available [here]


• Shan He, Huanhuan Chen, Xiaoli Li, Xin Yao. "Profiling of Mass Spectrometry Data for Ovarian Cancer Detection Using Negative Correlation Learning." In Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN'09), Cyprus, 2009. Available [here]


• Huanhuan Chen, Xin Yao. "Evolutionary random neural ensembles based on negative correlation learning." In Proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC'07), Singapore, 2007. Available [here]


• Huanhuan Chen, Peter Tiño, Xin Yao. "A Probabilistic Ensemble Pruning Algorithm." In Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (WICDM'06), Hong Kong, 2006. Available [here]

PhD Dissertation

• Huanhuan Chen : Diversity and Regularization in Neural Network Ensembles. School of Computer Science, University of Birmingham.
  (2011 IEEE Computational Intelligence Society Outstanding PhD Dissertation Award. )
  (British Computer Society Distinguished Dissertations Award (the Runner up)).