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Like the traditional machine learning, the multi-label learning is faced with the curse of dimensionality. Some feature selection algorithms have been proposed for multi-label learning, which either convert the multi-label feature selection problem into numerous single-label feature selection problems, or directly select features from the multi-label data set. However, the former omit the label dependency, or produce too many new labels leading to learning with significant difficulties; the latter, taking the global label dependency into consideration, usually select a few redundant or irrelevant features, because actually not all labels depend on each other, which may confuse the algorithm and degrade its classification performance. To select a more relevant and compact feature subset as well as explore the label dependency, a granular feature selection method for multi-label learning is proposed with a maximal correlation minimal redundancy criterion based on mutual information. The maximal correlation minimal redundancy criterion makes sure that the selected feature subset contains the most class-discriminative information, while in the meantime exhibits the least intra-redundancy. Granulation can help explore the label dependency. We study the relation of the label granularity and the performance on four data sets, and compare the proposed method with other three multi-label feature selection methods. The experimental results demonstrate that the proposed method can select compact and specific feature subsets, improve the classification performance and performs better than other three methods on the widely-used multi-label learning evaluation criteria. [ABSTRACT FROM AUTHOR]

Abstract: Feature selection (attribute reduction) from large-scale incomplete data is a challenging problem in areas such as pattern recognition, machine learning and data mining. In rough set theory, feature selection from incomplete data aims to retain the discriminatory power of original features. To address this issue, many feature selection algorithms have been proposed, however, these algorithms are often computationally time-consuming. To overcome this shortcoming, we introduce in this paper a theoretic framework based on rough set theory, which is called positive approximation and can be used to accelerate a heuristic process for feature selection from incomplete data. As an application of the proposed accelerator, a general feature selection algorithm is designed. By integrating the accelerator into a heuristic algorithm, we obtain several modified representative heuristic feature selection algorithms in rough set theory. Experiments show that these modified algorithms outperform their original counterparts. It is worth noting that the performance of the modified algorithms becomes more visible when dealing with larger data sets. [Copyright &y& Elsevier]