Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Markov-Miml: A Markov chain-based multi-instance multi-label learning algorithm

Language

English

Abstract

The main aim of this paper is to propose an efficient and novel Markov chain-based multi-instance multi-label (Markov-Miml) learning algorithm to evaluate the importance of a set of labels associated with objects of multiple instances. The algorithm computes ranking of labels to indicate the importance of a set of labels to an object. Our approach is to exploit the relationships between instances and labels of objects. The rank of a class label to an object depends on (i) the affinity metric between the bag of instances of this object and the bag of instances of the other objects, and (ii) the rank of a class label of similar objects. An object, which contains a bag of instances that are highly similar to bags of instances of the other objects with a high rank of a particular class label, receives a high rank of this class label. Experimental results on benchmark data have shown that the proposed algorithm is computationally efficient and effective in label ranking for MIML data. In the comparison, we find that the classification performance of the Markov-Miml algorithm is competitive with those of the three popular MIML algorithms based on boosting, support vector machine, and regularization, but the computational time required by the proposed algorithm is less than those by the other three algorithms. © 2012 Springer-Verlag London.

Keywords

Label ranking, Markov chain, Multi-instance multi-label data, Stationary probability distribution, Transition probability matrix

Publication Date

2013

Source Publication Title

Knowledge and Information Systems

Volume

37

Issue

1

Start Page

83

End Page

104

Publisher

Springer Verlag

DOI

10.1007/s10115-012-0567-9

Link to Publisher's Edition

http://dx.doi.org/10.1007/s10115-012-0567-9

ISSN (print)

02191377

ISSN (electronic)

02193116

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