期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 50, 期 9, 页码 4031-4042出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2923430
关键词
Feature extraction; Rough sets; Indexes; Uncertainty; Measurement uncertainty; Machine learning algorithms; Greedy algorithms; Feature selection; neighborhood; rough approximation; rough set; self-information
类别
资金
- National Natural Science Foundation of China [61572082, 61673396, 61773349]
- Liaoning Revitalization Talents Program [071717005]
- Foundation of Educational Committee of Liaoning Province [LZ2016003]
- Natural Science Foundation of Liaoning Province [20170540012, 20170540004]
The concept of dependency in a neighborhood rough set model is an important evaluation function for the feature selection. This function considers only the classification information contained in the lower approximation of the decision while ignoring the upper approximation. In this paper, we construct a class of uncertainty measures: decision self-information for the feature selection. These measures take into account the uncertainty information in the lower and the upper approximations. The relationships between these measures and their properties are discussed in detail. It is proven that the fourth measure, called relative neighborhood self-information, is better for feature selection than the other measures, because not only does it consider both the lower and the upper approximations but also the change of its magnitude is largest with the variation of feature subsets. This helps to facilitate the selection of optimal feature subsets. Finally, a greedy algorithm for feature selection has been designed and a series of numerical experiments was carried out to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm often chooses fewer features and improves the classification accuracy in most cases.
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