A modified AdaBoost method for one-class SVM and its application to novelty detection

One-Class Support Vector Machine (OCSVM) is a general approach for novelty detection in the fields of machine learning and pattern classification. At the same time, AdaBoost is a famous ensemble method which can improve the performance of its base classifiers. However, the base classifiers in the AdaBoost method prefer to be weak classifiers. Since OCSVM is regarded as a strong classifier, the traditional AdaBoost method may not improve the classification performance of OCSVM. Therefore, to construct the AdaBoost method for OCSVM, we modify the traditional AdaBoost method to make it fit for OCSVM. Experimental results on three synthetic data sets and eight UCI benchmark data sets demonstrate that the proposed method is superior to its related methods.