Semi-supervised shilling attacks detection method based on SVM-KNN
LV Chengsh · Computer Engineering and Applications Journal · 2013
Traditional support vector machine drops significantly when only a few labeled training samples is available.To address this problem,a new SVM-KNN classification method based on semi-supervised learning is proposed.In the first stage,use the few labeled training samples to train a weaker SVM classifier.And in the second stage,make use of the boundary vectors to improve the weaker SVM iteratively by introducing KNN.Using KNN classifier doesn't enlarge the number of training examples only,but also improves the quality of the new training samples which are transformed from the boundary vectors.Then the proposed model is used to shilling attacks detection on recommender systems,the experimental results show that the proposed method can improve the classification accuracy,effective and easy to use in the case of fewer labeled training samples.