Feature Selection Based on Neighborhood Self-Information

• Published in: IEEE transactions on cybernetics Vol. 50; no. 9; pp. 4031 - 4042
• Main Authors: Wang, Changzhong, Huang, Yang, Shao, Mingwen, Hu, Qinghua, Chen, Degang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation; Classification; Feature extraction; Feature selection; Greedy algorithms; Indexes; Machine learning algorithms; Mathematical analysis; Measurement uncertainty; neighborhood; Neighborhoods; rough approximation; rough set; Rough set models; Rough sets; self-information; Uncertainty

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 constr...