基于加权SVM主动学习的多标签分类

Multi-label Classification Based on Weighted SVM Active Learning

样本标记是一个重要但又比较耗时的过程。得到一个多标签分类器需要大量的训练样本,而手工为每个样本创建多个标签会存在一定困难。为尽可能降低标记样本的工作量,提出一种加权决策函数的主动学习方法,该方法同时考虑训练样本的数量和未知样本的置信度,使得分类器能在最小的成本下最快地达到比较满意的分类精度。

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.