Abnormality detection using weighed particle swarm optimization and smooth support vector machine

作者：Latchoumi T P; Parthiban Latha^*

来源：Biomedical Research-India, 2017, 28(11): 4749-4751.

• Smooth support vector machine (SSVM)
• Particle swarm optimization (PSO)
• Clustering
• Classification

In this paper, a new hybrid classification approach, which uses Weighted-Particle Swarm Optimization (WPSO) for data clustering in sequence with Smooth Support Vector Machine (SSVM) for classification is proposed. The performance of WPSO clustering is compared with K means and fuzzy methods using intercluster, intracluster and validity index. The accuracy of proposed WPSO-SSVM classification methodology are 83.76% for liver disorder, 98.42% for WBCD, 95.21% for mammographic mass data which are better than in existing literature.

• 出版日期2017