期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 48, 期 3, 页码 876-889出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2663838
关键词
Feature selection; label correlation; label-specific features; multilabel classification; shared features
类别
资金
- National Natural Science Foundation of China [61332016, 61620106009, U1636214, 61650202]
- National Basic Research Program of China (973 Program) [2015CB351800]
- Key Research Program of Frontier Sciences, Chinese Academy of Sciences [QYZDJ-SSW-SYS013]
- Program for Changjiang Scholars and Innovative Research Team in University of the Ministry of Education, China [IRT13059]
- U.S. National Science Foundation [1652107]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1652107] Funding Source: National Science Foundation
Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.
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