Neural Information Processing

The challenge in feature selection for time series lies in achieving similar prediction performance when compared with the original dataset. The method has to ensure that important information has not been lost by with feature selection for data reduction. We present a chaotic feature selection and reconstruction method based on statistical analysis for time series prediction. The method can also be viewed as a way for reduction of data through selection of most relevant features with the hope of reducing training time for learning algorithms. We employ cooperative neuro-evolution as a machine learning tool to evaluate the performance of the proposed method. The results show that our method gives a data reduction of up to 42 % with a similar performance when compared to the literature.

[1]  Adwait Ratnaparkhi,et al.  A Maximum Entropy Model for Part-Of-Speech Tagging , 1996, EMNLP.

[2]  Christopher D. Manning,et al.  Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger , 2000, EMNLP.

[3]  Shie Mannor,et al.  Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning , 2003, ICML.

[4]  Fernando Pereira,et al.  Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[5]  Michail G. Lagoudakis,et al.  Least-Squares Policy Iteration , 2003, J. Mach. Learn. Res..

[6]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[7]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[8]  Andrew W. Moore,et al.  Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time , 1993, Machine Learning.

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[11]  Nicholas K. Jong,et al.  Kernel-Based Models for Reinforcement Learning , 2006 .

[12]  Xin Xu,et al.  Kernel Least-Squares Temporal Difference Learning , 2006 .

[13]  Xin Xu,et al.  A Sparse Kernel-Based Least-Squares Temporal Difference Algorithm for Reinforcement Learning , 2006, ICNC.

[14]  Xin Xu,et al.  Kernel-Based Least Squares Policy Iteration for Reinforcement Learning , 2007, IEEE Transactions on Neural Networks.

[15]  R. Plomin,et al.  Genetic, environmental and gender influences on attachment disorder behaviours , 2007, British Journal of Psychiatry.

[16]  Alborz Geramifard,et al.  Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping , 2008, UAI.

[17]  Phuong-Thai Nguyen,et al.  Building a Large Syntactically-Annotated Corpus of Vietnamese , 2009, Linguistic Annotation Workshop.

[18]  Oanh Thi Tran,et al.  An Experimental Study on Vietnamese POS Tagging , 2009, 2009 International Conference on Asian Language Processing.

[19]  Oanh Thi Tran,et al.  Improving Vietnamese Word Segmentation and POS Tagging using MEM with Various Kinds of Resources , 2010 .

[20]  Bart De Schutter,et al.  Reinforcement Learning and Dynamic Programming Using Function Approximators , 2010 .

[21]  Le Minh Nguyen,et al.  A Semi-supervised Learning Method for Vietnamese Part-of-Speech Tagging , 2010, 2010 Second International Conference on Knowledge and Systems Engineering.

[22]  Yiannis Demiris,et al.  Echo State Gaussian Process , 2011, IEEE Transactions on Neural Networks.

[23]  Jochen J. Steil,et al.  Regularization and stability in reservoir networks with output feedback , 2012, Neurocomputing.

[24]  Benjamin Schrauwen,et al.  Reservoir Computing Trends , 2012, KI - Künstliche Intelligenz.

[25]  Kwong-Sak Leung,et al.  Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification , 2013, BMC Bioinformatics.

[26]  Peter Vrancx,et al.  Reinforcement Learning: State-of-the-Art , 2012 .

[27]  Hado van Hasselt,et al.  Reinforcement Learning in Continuous State and Action Spaces , 2012, Reinforcement Learning.

[28]  Zongben Xu,et al.  $L_{1/2}$ Regularization: A Thresholding Representation Theory and a Fast Solver , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Brendan T. O'Connor,et al.  Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters , 2013, NAACL.

[30]  Han Mi.  An Improved Echo State Network via L_1-Norm Regularization , 2014 .

[31]  Xin Zhou,et al.  Experience replay for least-squares policy iteration , 2014, IEEE/CAA Journal of Automatica Sinica.

[32]  Jatin Sharma,et al.  POS Tagging of English-Hindi Code-Mixed Social Media Content , 2014, EMNLP.

[33]  Akinori Nishihara,et al.  Evolutionary pre-training for CRJ-type reservoir of echo state networks , 2015, Neurocomputing.

[34]  Frans A. Henskens,et al.  A Modified Case-Based Reasoning Approach for Triaging Psychiatric Patients Using a Similarity Measure Derived from Orthogonal Vector Projection , 2015, ACALCI.

[35]  Yiannis Demiris,et al.  Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes , 2015, IEEE Transactions on Neural Networks and Learning Systems.