One-class Classifier Based on Support Vectors for Noisy Data by Using Chaotic Krill Herd Algorithm and Local Density

The purpose of one-class classification is to detect and separate target data from outlier. Support vector data description classifier is one of the one-class data classification methods. This method creates a hyper-sphere in feature space and tries to cover target data in the hyper-sphere. The hyper-sphere surface is the discernment boundary between target and outlier data. Determining appropriate radius and center for the sphere is an optimization problem. Existence of the noise in the data set and lack of attention to data density for choosing the center is the challenge of this method that triggered the mistake in determining of detection boundary. In the proposed classifier (KH-SVDD) we tried to search appropriate center of sphere with the use of chaotic krill herd optimization algorithm. Also, a weight is calculated for the effectiveness of the points on the classifier boundary with the use of local density of data points. This weight is an auxiliary parameter to detect target data from noise. The results of the experiments have been compared with state-of-the-art methods, which show superiority of the proposed method in noise detection.