2022
DOI: 10.1007/s40747-022-00882-8
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Feature selection based on self-information and entropy measures for incomplete neighborhood decision systems

Abstract: For incomplete datasets with mixed numerical and symbolic features, feature selection based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly. However, its evaluation function only considers the information contained in the lower approximation of the neighborhood decision, which easily leads to the loss of some information. To solve this problem, we construct a novel NMRS-based uncertain measure for feature selection, named neighborhood multi-granulation self-information-based pessimist… Show more

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Cited by 6 publications
(2 citation statements)
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“…The used neighborhood re- lation is still crisp which may fail to reflect the actual certainty in data. 3) NSIFS [35]: It is a forward greedy approach that applies the neighborhood self-information and entropy measures. It extends some information measures to neighborhood-based ones and can describe the feature significance from an uncertain perspective.…”
Section: A Configurationsmentioning
confidence: 99%
“…The used neighborhood re- lation is still crisp which may fail to reflect the actual certainty in data. 3) NSIFS [35]: It is a forward greedy approach that applies the neighborhood self-information and entropy measures. It extends some information measures to neighborhood-based ones and can describe the feature significance from an uncertain perspective.…”
Section: A Configurationsmentioning
confidence: 99%
“…A filter method based on neighborhood multi-granulation rough sets is proposed in [52] that uses a novel self-information measure for initial preprocessing followed by Fisher score to delete uncorrelated features. Another filter method called the Highest Wins was proposed in [33] for intrusion detection.…”
Section: Literaturementioning
confidence: 99%