Soft Computing Pattern Recognition: Principles, Integrations, and Data Mining

Relevance of fuzzy logic, artificial neural networks, genetic algorithms and rough sets to pattern recognition and image processing problems is described through examples. Different integrations of these soft computing tools are illustrated. Evolutionary rough fuzzy network which is based on modular principle is explained, as an example of integrating all the four tools for efficient classification and rule generation, with its various characterstics. Significance of soft computing approach in data mining and knowledge discovery is finally discussed along with the scope of future research.

[1]  Sankar K. Pal,et al.  Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing , 1999 .

[2]  Sankar K. Pal,et al.  Soft Computing for Image Processing , 2000 .

[3]  Roman Słowiński,et al.  Intelligent Decision Support , 1992, Theory and Decision Library.

[4]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition : methods that search for structures in data , 1992 .

[7]  Sankar K. Pal,et al.  Soft Computing in Case Based Reasoning , 2000, Springer London.

[8]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[9]  Sankar K. Pal,et al.  Neural Networks and Systolic Array Design , 2002 .

[10]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Witold Pedrycz,et al.  Presenting the special issue on Rough-neuro computing : Preface , 2001, Neurocomputing.

[13]  Sankar K. Pal,et al.  Rough fuzzy MLP: knowledge encoding and classification , 1998, IEEE Trans. Neural Networks.

[14]  Sankar K. Pal,et al.  Pattern Recognition: From Classical to Modern Approaches , 2001 .

[15]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[16]  Sankar K. Pal,et al.  Fuzzy Mathematical Approach to Pattern Recognition , 1986 .

[17]  Sankar K. Pal,et al.  Data mining in soft computing framework: a survey , 2002, IEEE Trans. Neural Networks.

[18]  R.P. Lippmann,et al.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[19]  Sankar K. Pal,et al.  Genetic Algorithms for Pattern Recognition , 2017 .

[20]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[21]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

[22]  Sankar K. Pal,et al.  Staging of cervical cancer with soft computing , 2000, IEEE Transactions on Biomedical Engineering.

[23]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[24]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[25]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[26]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[27]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.