Multi-label Classification Based on Weighted SVM Active Learning
Weijie Qiu · Jisuanji gongcheng · 2011
Manually creating multiple labels for each sample is very important but it is time-consuming.Manually creating multiple labels for each sample may become impractical when a very large amount of data is needed for training multi-label classifier.To minimize the human-labeling efforts,this paper proposes a weighted decision approach,the approach considers quantity and confidence of training samples,it can make the classifier need fewer samples,but achieve a comparative precision.