基于多粒度一致性邻域的多标记特征选择

Multi-label feature selection based on multi-granularity consistent neighborhood

多标记学习广泛应用于图像分类、疾病诊断等领域,然而特征的高维性给多标记分类算法带来时间负担、过拟合和性能低等问题.基于多粒度邻域一致性设计相应的多标记特征选择算法:首先利用标记空间和特征空间邻域一致性来粒化所有样本,并基于多粒度邻域一致性观点定义新的多标记邻域信息熵和多标记邻域互信息;其次,基于邻域互信息构建一个评价候选特征质量的目标函数用于评价每个特征的重要性;最后通过多个指标验证了所提算法的有效性.

Multi-label learning is widely used in image classification,disease diagnosis and other fields.However,the high dimension of features brings time burden,over fitting and low performance to multi-label classification algorithms.In this paper,a multi-label feature selection algorithm is designed based on multi-granularity neighborhood consistency.Firstly,all samples are granulated by using the neighborhood consistency of label space and feature space.Moreover,new multi-label neighborhood information entropy and multi-label neighborhood mutual information are defined based on the view of multi-granularity neighborhood consistency.Secondly,an objective function is constructed to evaluate the quality of candidate features based on multi-label new neighborhood mutual information,which is UvSed to evaluate the importance of each feature.The effectiveness of the proposed algorithm is verified by several measure criteria.