Machine Learning

, Volume 106, Issue 5, pp 671–694

Progressive random k-labelsets for cost-sensitive multi-label classification

Article

DOI: 10.1007/s10994-016-5600-x

Cite this article as:
Wu, YP. & Lin, HT. Mach Learn (2017) 106: 671. doi:10.1007/s10994-016-5600-x
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Abstract

In multi-label classification, an instance is associated with multiple relevant labels, and the goal is to predict these labels simultaneously. Many real-world applications of multi-label classification come with different performance evaluation criteria. It is thus important to design general multi-label classification methods that can flexibly take different criteria into account. Such methods tackle the problem of cost-sensitive multi-label classification (CSMLC). Most existing CSMLC methods either suffer from high computational complexity or focus on only certain specific criteria. In this work, we propose a novel CSMLC method, named progressive random k-labelsets (PRAkEL), to resolve the two issues above. The method is extended from a popular multi-label classification method, random k-labelsets, and hence inherits its efficiency. Furthermore, the proposed method can handle arbitrary example-based evaluation criteria by progressively transforming the CSMLC problem into a series of cost-sensitive multi-class classification problems. Experimental results demonstrate that PRAkEL is competitive with existing methods under the specific criteria they can optimize, and is superior under other criteria.

Keywords

Machine learning Multi-label classification Loss function Cost-sensitive learning Labelset Ensemble method 

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.National Taiwan UniversityTaipeiTaiwan

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