Combining Independent Component Analysis and Fuzzy Particle Swarm Optimization for Fuzzy Clustering
نویسندگان
چکیده
Feature selection is the process of removing the irrelevant features from the datasets and fuzzy clustering of microarray data are the most fascinating machine learning techniques in the real world. The main objective of this paper is selecting the independent components of the microarray data using Independent Component Analysis in order to improve the effectiveness and accuracy of the Fuzzy Particle Swarm Optimization algorithms. To show the effectiveness of the fuzzy clustering is used four gene microarray datasets namely Colon Cancer, Prostate Cancer, Lung Cancer II and HighGrade Glioma. Experimental results are demonstrated on four datasets those features are selected by the proposed ICA method to improving efficient of the classification. The proposed method ICA-FPSO is produced high accuracy and least computational time which is compared with Fuzzy Particle Swarm Optimization and Particle Swarm Optimization.
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