NDLI: Learning Label Specific Features for Multi-label Classification

Content Provider	IEEE Xplore Digital Library
Author	Jun Huang Guorong Li Qingming Huang Xindong Wu
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Inf. Eng., Hefei Univ. of Technol., Hefei, China (Xindong Wu) \|\| Sch. of Comput. & Control Eng., Univ. of Chinese Acad. of Sci., Beijing, China (Jun Huang; Guorong Li; Qingming Huang)
Abstract	Binary relevance (BR) is a well-known framework for multi-label classification. It decomposes multi-label classification into binary (one-vs-rest) classification subproblems, one for each label. The BR approach is a simple and straightforward way for multi-label classification, but it still has several drawbacks. First, it does not consider label correlations. Second, each binary classifier may suffer from the issue of class-imbalance. Third, it can become computationally unaffordable for data sets with many labels. Several remedies have been proposed to solve these problems by exploiting label correlations between labels and performing label space dimension reduction. Meanwhile, inconsistency, another potential drawback of BR, is often ignored by researchers when they construct multi-label classification models. Inconsistency refers to the phenomenon that if an example belongs to more than one class label, then during the binary training stage, it can be considered as both positive and negative example simultaneously. This will mislead binary classifiers to learn suboptimal decision boundaries. In this paper, we seek to solve this problem by learning label specific features for each label. We assume that each label is only associated with a subset of features from the original feature set, and any two strongly correlated class labels can share more features with each other than two uncorrelated or weakly correlated ones. The proposed method can be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Comparison with the state-of-the-art approaches manifests competitive performance of our proposed method.
Starting Page	181
Ending Page	190
File Size	338359
Page Count	10
File Format	PDF
ISSN	15504786
e-ISBN	9781467395045
DOI	10.1109/ICDM.2015.67
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Training Prediction algorithms Bayes methods Algorithm design and analysis Data mining Computers
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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