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Inference System Modeling Using Hybrid Evolutionary Algorithm: Application to breast cancer data set

Author(s): Amin Einipour

Journal: International Journal of Computer Science and Network Solutions
ISSN 2345-3397

Volume: 1;
Issue: 1;
Start page: 21;
Date: 2013;
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Keywords: Classification | Fuzzy If-Then Rules | ACO Algorithm

This paper addresses the well-known classification task of data mining, where the objective is to predict theclass which an example belongs to. Discovered knowledge is expressed in the form of high-level, easy-tointerpretclassification rules. In order to discover classification rules, we propose a hybrid metaheuristic/fuzzy system. In this paper we use an Ant Colony Optimization method as meta-heuristicalgorithm which extracts optimized fuzzy if-then rules for classification patterns. Fuzzy rules are desirablebecause of their interpretability by human experts. Ant colony algorithm is employed as evolutionaryalgorithm to optimize the obtained set of fuzzy rules. Results on breast cancer data set from UCI machinelearning repository show that the proposed approach would be capable of classifying cancer patterns withhigh accuracy rate in addition to adequate interpretability of extracted rules.
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