基于偏袒性半监督集成的SVM主动反馈方案

SVM Active Feedback Scheme Using Semi-Supervised Ensemble with Bias

现有的SVM主动反馈算法普遍受到小样本问题和不对称分布问题的制约.针对这些问题,文中提出一种基于偏袒性半监督集成的SVM主动反馈技术.该算法在集成学习框架中使用未标记数据以增加个体分类器之间的差异性,从而获得高效的集成分类模型.同时,高效的集成分类模型更有利于寻找富有信息样本,进而也提高主动反馈的效率.此外,文中还设计一种偏袒加权策略,使得集成分类模型对正样本给予更大的关注程度,以应对正负样本间的不对称分布问题.实验结果表明,偏袒性半监督集成可有效改进SVM主动反馈的性能,且文中算法的检索精度明显优于其它同类相关反馈算法.

Most SVM-based active learning methods are challenged by the small sample problem and the asymmetric distribution problems.A SVM-based active relevance feedback scheme is presented which deals with SVM ensemble under semi-supervised setting to augment the diversity among the individual SVM classifiers,thus a powerful ensemble classification model is obtained.Meanwhile,the powerful ensemble model is helpful to identify the most informative images for active learning.Moreover,aggregation method,termed as bias-weighting,is used within the semi-supervised ensemble framework to tackle the asymmetric distribution between positive and negative samples.Under the influence of bias-weighting,the ensemble classification model pays more attention on the positive samples than the negative ones.Experimental results validate the superiority of the presented scheme over several existing active learning methods.