کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
535760 | 870374 | 2013 | 9 صفحه PDF | دانلود رایگان |
Recently, classification tasks that naturally emerge in multi-label domains, such as text categorization, automatic scene annotation, and gene function prediction, have attracted great interest. As in traditional single-label classification, feature selection plays an important role in multi-label classification. However, recent feature selection methods require preprocessing steps that transform the label set into a single label, resulting in subsequent additional problems. In this paper, we propose a feature selection method for multi-label classification that naturally derives from mutual information between selected features and the label set. The proposed method was applied to several multi-label classification problems and compared with conventional methods. The experimental results demonstrate that the proposed method improves the classification performance to a great extent and has proved to be a useful method in selecting features for multi-label classification problems.
► We propose a multivariate mutual information-based feature selection for multi-label classification.
► Label interactions without resorting to problem transformation have been considered.
► The calculation of high-dimensional entropy is decomposed into a cumulative sum of multivariate mutual information.
Journal: Pattern Recognition Letters - Volume 34, Issue 3, 1 February 2013, Pages 349–357