期刊
INFORMATION SCIENCES
卷 372, 期 -, 页码 256-275出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.08.039
关键词
Feature selection; Streaming labels; Multi-label learning; Supervised learning
资金
- National Natural Science Foundation of China [61672272, 61303131, 61432011]
- Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China [IRT13059]
- China Postdoctoral Science Foundation [2015M581298]
In this paper, we study a novel and challenging issue, multi-label feature selection with streaming labels, in which the number of labels is unknown in advance, and the size of the feature set is constant. In this problem, we assume that the labels arrive one at a time, and the learning task is to rank features iteratively when a new label arrives. Traditional multi-label feature selection methods cannot perform well in this scenario. Therefore, we present an optimization framework where the weight of each label's feature rank list and the final feature rank list are defined as two sets of unknown variables. The objective is to minimize the overall weighted deviation between the final feature rank list and each label's feature rank list. Extensive experiments on benchmark data sets demonstrate that the proposed method outperforms other multi-label feature selection methods. (C) 2016 Elsevier Inc. All rights reserved.
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