A hybrid approach to design efficient learning classifiers

Recently, use of a Learning Classifier System (LCS) has become promising method for performing classification tasks and data mining. For the task of classification, the quality of the rule set is usually evaluated as a whole rather than evaluating the quality of a single rule. The present investigation proposes a hybrid of the C4.5 rule induction algorithm and a GA (Genetic Algorithm) approach to extract an accuracy based rule set. At the initial stage, C4.5 is applied to a classification problem to generate a rule set. Then, the GA is used to refine the rules learned. Using eight well-known data sets, it has been shown that the present work, in comparison to C4.5 alone and UCS, provides a marked improvement in a number of cases.

[1]  Michael J. Pazzani,et al.  Knowledge discovery from data? , 2000, IEEE Intell. Syst..

[2]  Rudy Setiono,et al.  Extracting Rules from Neural Networks by Pruning and Hidden-Unit Splitting , 1997, Neural Computation.

[3]  Michael J. Shaw,et al.  A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based Learning , 1994, INFORMS J. Comput..

[4]  Kamel Faraoun,et al.  Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection , 2006, Int. Arab J. Inf. Technol..

[5]  Alex Alves Freitas,et al.  Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution , 2001, PKDD.

[6]  Novruz Allahverdi,et al.  Rule extraction from trained adaptive neural networks using artificial immune systems , 2009, Expert Syst. Appl..

[7]  Dr. Alex A. Freitas.  Data Mining and Knowledge Discovery with Evolutionary Algorithms , 2002, Natural Computing Series.

[8]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  Hongjun Lu,et al.  Effective Data Mining Using Neural Networks , 1996, IEEE Trans. Knowl. Data Eng..

[11]  Ester Bernadó-Mansilla,et al.  Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks , 2003, Evolutionary Computation.

[12]  Wai Keung Wong,et al.  A hybrid model using genetic algorithm and neural network for classifying garment defects , 2009, Expert Syst. Appl..

[13]  Hisao Ishibuchi,et al.  Linguistic rule extraction from neural networks and genetic-algorithm-based rule selection , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[14]  Ivan Bratko,et al.  Machine learning applied to quality management - A study in ship repair domain , 2007, Comput. Ind..

[15]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

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

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

[18]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[19]  Michel Parent,et al.  The French Program La Route Automatisée , 2000, IEEE Intell. Syst..

[20]  Pei-Chann Chang,et al.  A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry , 2006, Decis. Support Syst..

[21]  Jerzy W. Grzymala-Busse,et al.  A Comparison of Two Approaches to Data Mining from Imbalanced Data , 2004, J. Intell. Manuf..

[22]  Riyaz Sikora,et al.  Learning control strategies for chemical processes: a distributed approach , 1992, IEEE Expert.

[23]  Wing-Keung Wong,et al.  A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T-S fuzzy neural network , 2009, Expert Syst. Appl..

[24]  Michael J. Shaw,et al.  A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[25]  Joydeep Ghosh,et al.  Mutual information feature extractors for neural classifiers , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[26]  Himika Biswas,et al.  SPID4.7: Discretization Using Successive Pseudo Deletion at Maximum Information Gain Boundary Points , 2005, SDM.

[27]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[28]  Tzung-Pei Hong,et al.  Learning cross-level certain and possible rules by rough sets , 2008, Expert Syst. Appl..

[29]  Foster J. Provost,et al.  A Survey of Methods for Scaling Up Inductive Algorithms , 1999, Data Mining and Knowledge Discovery.

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

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

[32]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

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

[34]  Stewart W. Wilson.  Classifier Fitness Based on Accuracy , 1995, Evolutionary Computation.

[35]  Hiroyuki Shimizu,et al.  Handwritten Kanji recognition with the LDA method , 1997, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[36]  D. J. Newman,et al.  UCI Repository of Machine Learning Database , 1998 .

[37]  Wai Keung Wong,et al.  A fashion mix-and-match expert system for fashion retailers using fuzzy screening approach , 2009, Expert Syst. Appl..

[38]  Philip K. Chan,et al.  Systems for Knowledge Discovery in Databases , 1993, IEEE Trans. Knowl. Data Eng..

[39]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .