An Artificial Immune System for Fuzzy-Rule Induction in Data Mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm.

[1]  Heitor Silvério Lopes,et al.  AN EVOLUTIONARY APPROACH TO SIMULATE COGNITIVE FEEDBACK LEARNING IN MEDICAL DOMAIN , 1997 .

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

[3]  A. B. Watkins,et al.  A resource limited artificial immune classifier , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  W. Pedrycz,et al.  An introduction to fuzzy sets : analysis and design , 1998 .

[6]  D. Dasgupta,et al.  The fuzzy artificial immune system: motivations, basic concepts, and application to clustering and Web profiling , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[7]  Alex Alves Freitas,et al.  Revisiting the Foundations of Artificial Immune Systems: A Problem-Oriented Perspective , 2003, ICARIS.

[8]  Jennie Malboeuf.  Algorithm , 1994, Neurology.

[9]  Deborah R. Carvalho,et al.  A Genetic Algorithm With Sequential Niching For Discovering Small-disjunct Rules , 2002, GECCO.

[10]  Fabio A. González,et al.  An Imunogenetic Technique To Detect Anomalies In Network Traffic , 2002, GECCO.

[11]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[12]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[13]  Hisao Ishibuchi,et al.  Effect of rule weights in fuzzy rule-based classification systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[14]  Witold Pedrycz,et al.  An Introduction to Fuzzy Sets , 1998 .

[15]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[16]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[17]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[18]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .