Learning One-class Support Vector Machine by Using Artificial Bee Colony Algorithm and Its Application for Disease Classification
Ming‐Huwi Horng, Yu-Lun Hong, Yung‐Nien Sun, Zhe-Yuan Zhan, Chen-Yu Hong · 2019
The one-classification support vector (OCSVM) is a variant of SVM which only uses the positive class sample set in training stage. It has been widely used in the applications of disease diagnose, handwritten signature verification, remote sensing and document classification. However, there are many parameters needed to regulate. The mistake of parameter setting makes OCSVM it to be not effectiveness. Therefore, in this paper we proposed a learning algorithm based on the artificial bee colony algorithm to select the parameters. The construction algorithm of OSCVM is called the artificial bee colony based OSCVM (ABC-OCSVM) algorithm. Experimental results of two medical datasets of UCI data repository showed that our proposed ABC-OCSVM method outperforms the conventional LIBSVM package.