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 language | English |
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Title of host publication | The 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery |
Publisher | IEEE Press |
Pages | 1197-1202 |
Number of pages | 6 |
ISBN (Print) | 978-153862165-3 |
DOIs | |
Publication status | Published - 21 Jun 2017 |
Event | 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery - Guilin, China Duration: 29 Jul 2017 → 31 Jul 2017 |
Conference
Conference | 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery |
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Abbreviated title | ICNC-FSKD 2017 |
Country/Territory | China |
City | Guilin |
Period | 29 Jul 2017 → 31 Jul 2017 |