Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules

Authors:
Mohammad Javad Abdi;Davar Giveki
Affiliations:
Department of Computer Sciences, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, P.O. Box: 14115-134, Iran;Department of Computer Sciences, University of Saarland, Saarbrucken, Germany
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

Citing 18
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Abstract

In this paper, we develop a diagnosis model based on particle swarm optimization (PSO), support vector machines (SVMs) and association rules (ARs) to diagnose erythemato-squamous diseases. The proposed model consists of two stages: first, AR is used to select the optimal feature subset from the original feature set; then a PSO based approach for parameter determination of SVM is developed to find the best parameters of kernel function (based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy, and PSO is a promising tool for global searching). Experimental results show that the proposed AR_PSO-SVM model achieves 98.91% classification accuracy using 24 features of the erythemato-squamous diseases dataset taken from UCI (University of California at Irvine) machine learning database. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.