In order to reduce the negative influence of outliers on the model of support vector data description (SVDD) when the training dataset contains both normal samples and outliers which are all labeled as target class

a one-cluster kernel possibilistic C-means based SVDD method for outlier detection is proposed.In this paper

each sample of the training dataset is assigned a confidence level based on the membership degree of each sample belonging to the normal class

which is obtained through the one-cluster kernel PCM clustering.The proposed algorithm incorporates the confidence levels into the training model to reduce the importance of the samples which have less confidence levels.The experimental results show that the proposal significantly improves the effect of outlier detection

compared with the existing SVDD-based outlier detection methods.