Abstract:
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.
Key words:
active learning,
multi-label,
Support Vector Machine(SVM),
training samples
摘要:
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。
关键词:
主动学习,
多标签,
支持向量机,
训练样本
CLC Number:
LIU Duan-Yang, QIU Wei-Jie. Multi-label Classification Based on Weighted SVM Active Learning[J]. Computer Engineering, 2011, 37(8): 181-182.
刘端阳, 邱卫杰. 基于加权SVM主动学习的多标签分类[J]. 计算机工程, 2011, 37(8): 181-182.