Evolving Fuzzy Rule-based Classifiers

A novel approach to on-line classification based on fuzzy rules with an open/evolving structure is introduced in this paper. This classifier can start `from scratch', learning and adapting to the new data samples or from an initial rule-based classifier that can be updated based on the new information contained in the new samples. It is suitable for real-time applications such as classification streaming data, robotic applications, e.g., target and landmark recognition, real-time machine health monitoring and prognostics, fault detection and diagnostics, etc. Each prototype is a data sample that represents the focal point of a fuzzy rule per class and is selected based on the data density by an incremental and evolving procedure. This approach is transparent, linguistically interpretable, and applicable to both fully unsupervised and partially supervised learning. It has been validated by two well known benchmark problems and by real-life data in a parallel paper. The contributions of this paper are: i) introduction of the concept of evolving (open structure) classification (eClass) of streaming data; ii) experiments with well known benchmark classification problems (Iris and wine reproduction data sets)

[1]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[2]  P. Angelov,et al.  Evolving Fuzzy Systems from Data Streams in Real-Time , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[3]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[4]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[6]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[7]  Hwee Tou Ng,et al.  Bayesian online classifiers for text classification and filtering , 2002, SIGIR '02.

[8]  Plamen P. Angelov,et al.  Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[9]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[10]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[11]  Plamen P. Angelov,et al.  A fuzzy controller with evolving structure , 2004, Inf. Sci..

[12]  Edwin Lughofer,et al.  An approach to model-based fault detection in industrial measurement systems with application to engine test benches , 2006 .

[13]  Ruoming Jin,et al.  Efficient decision tree construction on streaming data , 2003, KDD '03.

[14]  Frank Klawonn,et al.  Evolving Extended Naive Bayes Classifiers , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[15]  Plamen Angelov,et al.  An Approach to Autonomous Self-localization of a Mobile Robot in Completely Unknown Environment using Evolving Fuzzy Rule-based Classifier , 2007 .

[16]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[17]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[18]  Shaoning Pang,et al.  Incremental linear discriminant analysis for classification of data streams , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Edwin Lughofer,et al.  FLEXFIS: A Variant for Incremental Learning of Takagi-Sugeno Fuzzy Systems , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[20]  Hisao Ishibuchi,et al.  Voting in fuzzy rule-based systems for pattern classification problems , 1999, Fuzzy Sets Syst..

[21]  Martin V. Butz,et al.  Rule-Based Evolutionary Online Learning Systems - A Principled Approach to LCS Analysis and Design , 2006, Studies in Fuzziness and Soft Computing.

[22]  David G. Stork,et al.  Pattern Classification , 1973 .

[23]  P. Angelov,et al.  Evolving rule-based models: A tool for intelligent adaptation , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[24]  Plamen Angelov,et al.  Evolving Rule-Based Models: A Tool For Design Of Flexible Adaptive Systems , 2002 .

[25]  Plamen P. Angelov,et al.  Identification of evolving fuzzy rule-based models , 2002, IEEE Trans. Fuzzy Syst..

[26]  Geoff Hulten,et al.  Catching up with the Data: Research Issues in Mining Data Streams , 2001, DMKD.

[27]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[28]  Ronald R. Yager,et al.  Learning of Fuzzy Rules by Mountain Clustering , 1992 .

[29]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[30]  Plamen Angelov,et al.  Identification of Evolving Rule-based Models. , 2002 .

[31]  Nikola K. Kasabov,et al.  Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[32]  Albert Sydney Hornby,et al.  Oxford advanced learner\'s dictionary of current English / A S Hornby with A P Cowie, A C Gimson , 1975 .