Fuzzy rule-based classifier for microarray gene expression data by using a multiobjective PSO-based approach

In this article a fuzzy rule-based classifier has been designed on the framework of multiobjective Particle Swarm Optimization. The proposed approach is applied on microarray gene expression data to obtain genes with significant expression with respect to two different classes. Two fuzzy sets are represented with the linguistic values “high” and “low”. On the training dataset, the proposed approach is applied for the purpose of deriving good classification rules. To be precise, the good rules are those that have less attributes in the antecedent part and provide maximum accuracy. Moreover we also consider the existing redundancy among the selected rules which should be minimized. Here the underlying structure is modeled using multiobjective PSO with the support of non-dominated sorting and crowding distance sorting. The first objective is to maximize the classification accuracy and second objective is to minimize the rule-base complexity (number of rules and average rule length) and the redundancy of the rules. The performance of the proposed algorithm is compared with that of single objective versions, Support Vector machine classifier and Bayes classifier on several real-life datasets.