A new approach to exploring rough set boundary region for feature selection

Rong Li, Yanpeng Qu, Ansheng Deng, Changjing Shang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Feature selection offers a crucial way to reduce the irrelevant and misleading features for a given problem, while retaining the underlying semantics of selected features. Whilst maintaining the quality of problem-solving (e.g., classification), a superior feature selection process should be reduce the number of attributes as much as possible. In this paper, a non-unique decision value (NDV), which is defined as the number of attribute values that can lead to non-unique decision values, is proposed to rapidly capture the uncertainty in the boundary region of a granular space. Also, as an evaluator of the selected feature subset, an NDV-based differentiation entropy (NDE) is introduced to implement a novel feature selection process. The experimental results demonstrate that the selected features by the proposed approach outperform those attained by other state-of-the-art feature selection methods, in respect of both the size of reduction and the classification accuracy
Original languageEnglish
Title of host publicationThe 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
PublisherIEEE Press
Pages1197-1202
Number of pages6
ISBN (Print)978-153862165-3
DOIs
Publication statusPublished - 21 Jun 2017
Event13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery - Guilin, China
Duration: 29 Jul 201731 Jul 2017

Conference

Conference13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
Abbreviated titleICNC-FSKD 2017
Country/TerritoryChina
CityGuilin
Period29 Jul 201731 Jul 2017

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