Genetic network programming for fuzzy association rule-based classification

Authors:
Karla Taboada;Shingo Mabu;Eloy Gonzales;Kaoru Shimada;Kotaro Hirasawa
Affiliations:
Graduate School of Information, Production and Systems, Waseda University, Kitakyushushi, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushushi, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushushi, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushushi, Fukuoka, Japan;Graduate School of Information, Production and Systems, Waseda University, Kitakyushushi, Fukuoka, Japan
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 6
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Data Mining and Knowledge Discovery with Evolutionary Algorithms

Data Mining and Knowledge Discovery with Evolutionary Algorithms
A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning

Evolutionary Computation
Effectiveness of fuzzy discretization for class association rule-based classification

ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Association rule mining with chi-squared test using alternate genetic network programming

ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel classification approach that integrates fuzzy classification rules and Genetic Network Programming (GNP). A fuzzy discretization technique is applied to transform the dataset, particularly for dealing with quantitative attributes. GNP is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees of Genetic Algorithms (GA) and Genetic Programming (GP), respectively. This feature contributes to creating quite compact programs and implicitly memorizing past action sequences. Therefore, in the proposed method, taking the GNP's structure into account 1) extraction of fuzzy classification rules is done without identifying frequent itemsets used in most Apriori-based data mining algorithms, 2) calculation of the support, confidence and χ2 value is made in order to quantify the significance of the rules to be integrated into the classifier, 3) fuzzy membership values are used for fuzzy classification rules extraction, 4) fuzzy rules are mined through generations and stored in a general pool. On the other hand, parameters of the membership functions are evolved by non-uniform mutation in order to perform a more global search in the space of candidate membership functions. The performance of our algorithm has been compared with other relevant algorithms and the experimental results have shown the advantages and effectiveness of the proposed model.