DMCA
Random k-Labelsets for Multi-Label Classification (2010)
| Venue: | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
| Citations: | 30 - 2 self |
BibTeX
@MISC{Tsoumakas10randomk-labelsets,
author = {Grigorios Tsoumakas and Ioannis Katakis and Ioannis Vlahavas},
title = { Random k-Labelsets for Multi-Label Classification},
year = {2010}
}
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Abstract
A simple yet effective multi-label learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicate that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multi-label learning methods.
Keyphrases
ieee transaction knowledge data engineering multi-label classification random k-labelsets large number training example corresponding classifier computational efficiency empirical evidence application domain different class value exhibit competitive performance random labelsets label powerset effective multi-label learning method single-label classification task predictive performance initial set high-performing multi-label learning method distinct combination small random subset time many class
