关键词: 特征选择, 分层分类, 标记层次结构, 标记粒度, 递归正则化, 稀疏优化, 全局最优解

Abstract:

The task of classification learning using hierarchy of categories in data exists widely in many practical applications such as disease diagnosis, image annotation, etc. However, the high dimensionality of data feature space makes hierarchical classification learning confront problems such as high time and space complexity. In addition, existing research works assume that the training set label granularity is sufficiently fine-grained, which is contradictory to the actual hierarchical classification learning, i.e., dividing fine-grained labels is costly and ambiguity exists among category labels. To solve the above problems, we propose a coarse-to-fine hierarchical feature selection algorithm. We consider intra-class consistency and inter-sibling variability to select representative features and the unknown fine-grained labels of the training samples are predicted during feature selection. Experimental results on seven benchmark datasets show that the proposed algorithm outperforms some advanced comparative algorithms in classification performance and can handle the case where the label granularity is not fine-grained enough.

Key words: feature selection, hierarchical classification, label hierarchical structure, label granularity, recursive regularization, sparse optimization, global optimal solution