A SVM active learning method based on confidence, KNN and diversity
Yan Qiu Leng, Xinyan Xu, Chengli Sun, Chuanfu Cheng, Honglin Wan, Jing Fang, Dengwang Li · 2015
Audio is an important part of multimedia, and it has many useful applications in real life. Audio event classification is a key technology in audio management and application. Supervised audio event classification requires labeling large amounts of samples, while manual labeling is a very time-consuming work. In this paper we propose SVMCKNND, an active learning method for SVM classifier, to deal with the labeling problem in audio event classification. For SVMCKNND, in each iteration, first, a low-confidence region is delimited; then based on KNN, the samples that are more likely to be on the true class boundary are taken as the informative ones; finally, redundancy that exists in the informative samples is reduced to further decrease manual labeling workload. Experimental results show that SVMCKNNDperforms better than another two SVM active learning algorithms, especially in classifying small-sample audio events.