MLHN: A Hypernetwork Model for Multi-Label Classification
| Autor: | Xiao-Feng Xie, Chong Ho Lee, Kai-Wei Sun |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Multi-label classification
Computational complexity theory business.industry Pattern recognition Machine learning computer.software_genre Task (computing) Statistical classification ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Simple (abstract algebra) Scalability Computer Vision and Pattern Recognition Artificial intelligence business computer Software Mathematics |
| Zdroj: | International Journal of Pattern Recognition and Artificial Intelligence. 29:1550020 |
| ISSN: | 1793-6381 0218-0014 |
| DOI: | 10.1142/s0218001415500202 |
| Popis: | Multi-label classification has attracted significant attentions in machine learning. In multi-label classification, exploiting correlations among labels is an essential but nontrivial task. First, labels may be correlated in various degrees. Second, the scalability may suffer from the large number of labels, because the number of combinations among labels grows exponentially as the number of labels increases. In this paper, a multi-label hypernetwork (MLHN) is proposed to deal with these problems. By extending the traditional hypernetwork model, MLHN can represent arbitrary order correlations among labels. The classification model of MLHN is simple and the computational complexity of MLHN is linear with respect to the number of labels, which contribute to the good scalability of MLHN. We perform experiments on a variety of datasets. The results illustrate that the proposed MLHN achieves competitive performances against state-of-the-art multi-label classification algorithms in terms of both effectiveness and scalability with respect to the number of labels. |
| Databáze: | OpenAIRE |
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