Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork

Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are...

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Vydané v:IEEE transactions on knowledge and data engineering Ročník 28; číslo 9; s. 2438 - 2451
Hlavní autori: Sun, Kai Wei, Lee, Chong Ho, Wang, Jin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Algorithms
Categorization
Classification
Classification algorithms
Computation
Computational complexity
Correlation
hypernetwork
label correlations
Labels
Learning
multilabel learning
Scalability
Sun
Tasks
Training data
ISSN:1041-4347, 1558-2191
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Shrnutí:Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2016.2566621