http://dx.doi.org/10.1016/j.knosys.2014.02.012">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Multi-label collective classification via Markov chain based learning method

Language

English

Abstract

In this paper, we study the problem of multi-label collective classification (MLCC) where instances are related and associated with multiple class labels. Such correlation of class labels among interrelated instances exists in a wide variety of data, e.g., a web page can belong to multiple categories since its semantics can be recognized in different ways, and the linked web pages are more likely to have the same classes than the unlinked pages. We propose an effective and novel Markov chain based learning method for MLCC problems. Our idea is to model the problem as a Markov chain with restart on transition probability graphs, and to propagate the ranking score of labeled instances to unlabeled instances based on the affinity among instances. The affinity among instances is set up by explicitly using the attribute features derived from the content of instances as well as the correlation features constructed from the links of instances. Intuitively, an instance which contains linked neighbors that are highly similar to the other instances with a high rank of a particular class label, has a high chance of this class label. Extensive experiments have been conducted on two DBLP datasets to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed algorithm is shown to be better than those of the binary relevance multi-label algorithm, collective classification algorithms (wvRN, ICA and Gibbs), and the ICML algorithm for the tested MLCC problems. © 2014 Elsevier B.V. All rights reserved.

Keywords

Collective classification, Machine learning, Markov chain with restart, Multi-label collective classification, Multi-label learning

Publication Date

2014

Source Publication Title

Knowledge-Based Systems

Volume

63

Start Page

1

End Page

14

Publisher

Elsevier

ISSN (print)

09507051

ISSN (electronic)

18727409

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