A triple-random ensemble classification method for mining multi-label data
Nasierding, Gulisong, Kouzani, Abbas Z. and Tsoumakas, Grigorios 2010, A triple-random ensemble classification method for mining multi-label data, in ICDMW 2010 : Proceedings of 10th IEEE International Conference on Data Mining Workshops, IEEE Computer Society, Sydney, NSW, pp. 49-56.
This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.
Field of Research
090609 Signal Processing 080109 Pattern Recognition and Data Mining
Socio Economic Objective
890301 Electronic Information Storage and Retrieval Services
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