A SVM active learning method based on confidence, KNN and diversity

• Published in: 2015 IEEE International Conference on Multimedia and Expo (ICME) pp. 1 - 6
• Main Authors: Yan Leng, Xinyan Xu, Chengli Sun, Chuanfu Cheng, Honglin Wan, Jing Fang, Dengwang Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2015
• Subjects: Active learning; confidence; diversity; KNN; Labeling; Learning systems; Manuals; Redundancy; Speech; Support vector machines; SVM; Training
• ISSN: 1945-7871; 1945-788X
• DOI: 10.1109/ICME.2015.7177527

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 SVM CKNND , an active learning method for SVM classifier, to deal with the labeling problem in audio event classification. For SVM CKNND , 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 SVM CKNND performs better than another two SVM active learning algorithms, especially in classifying small-sample audio events.