Label Distribution Feature Selection Based on Mutual Information in Fuzzy Rough Set Theory

As we all know, the multi-label learning is faced with the "dimension disaster", and the label distribution learning is actually faced with the same problem. Therefore, it is necessary to perform some appropriate pre-processing on the data before the label distribution learning, such as feature selection. Nowadays, researchers have proposed many feature selection algorithms for multi-label learning, while feature selection algorithms for label distribution learning are few. The difference between label distribution learning and multi-label learning is that in the existing multi-label classification methods, the importance of each label is considered to be the same, but in actual cases, different labels has different importance when they are describing the same instance, that is, there is an imbalance between the labels. Therefore, this paper proposed a feature selection algorithm based on information measures in fuzzy rough set theory for label distribution learning. The mutual information in fuzzy rough set theory is used to calculate the correlation between features and labels, and then a feature subset is selected by correlation maximization and redundancy minimization strategy. Finally, the experimental results demonstrate that the proposed feature selection algorithm is able to select valid subsets of features.