For solving multi-label classification problems, traditional random K-labelsets method has two main drawbacks: 1) the randomly selected label set may result in highly imbalanced data for single-label multi-class learning, and 2) the dependency relations among different labels in the same label set may cause serious information redundancy and overlap. Both of these two drawbacks can affect the generalization capability of the multi-label learner. In order to overcome these two problems, in this paper, we propose a K-labelsets ensemble method based on mutual information and joint entropy. First, the mutual information and joint entropy are adopted to evaluate the redundancy level and imbalance level of each K-labelset. Then, disjoint sampling is performed iteratively, where during each iteration, a number of K-labelsets with low mutual information are retained to be the candidates, and the one with the highest joint entropy is selected. Afterwards, for each selected K-labelset, the label powerset method is employed and a multi-class classification model is constructed. Finally, the multi-class models on different K-labelsets are integrated, and a voting based ensemble model is generated to perform the predictions for unseen samples. We conduct extensive experiments on real-world multi-label data sets. Experimental results demonstrate the effectiveness of the proposed method.
|Title of host publication||IEEE International Conference on Fuzzy Systems|
|Publication status||Published - Jul 2018|
Bibliographical noteThis work was supported in part by the National Natural Science Foundation of China (Grant 61772344, Grant 61732011, Grant 61672443, and Grant 6171101518), in part by the Natural Science Foundation of SZU (Grant 827-000140, Grant 827-000230, and Grant 85303-00000260), in part by the Interdisciplinary Innovation Team of Shenzhen University, in part by Guangdong Province 2014GKXM054, and in part by Hong Kong RGC General Research Funds 9042489 (CityU 11206317) and 9042322 (CityU 11200116).
- Multi-label learning
- Mutual information