Min-uncertainty & max-certainty criteria of neighborhood rough-mutual feature selection

dc.contributor.authorFoithong, Sombut
dc.contributor.authorSrinil, Phaitoon
dc.contributor.authorPinngern, Ouen
dc.contributor.authorAttachoo, Boonwat
dc.date.accessioned2026-08-06T10:15:44Z
dc.date.available2026-08-06T10:15:44Z
dc.date.issued2017-01-01
dc.description.abstractFeature Selection (FS) is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Most existing FS methods based on rough set theory use the dependency function for evaluating the goodness of a feature subset. However, these FS methods may unsuccessfully be applied on dataset with noise, which determine only information from a positive region but neglect a boundary region. This paper proposes a criterion of the maximal lower approximation information (Max-Certainty) and minimal boundary region information (Min-Uncertainty), based on neighborhood rough set and mutual information for evaluating the goodness of a feature subset. We combine this proposed criterion with neighborhood rough set, which is directly applicable to numerical and heterogeneous features, without involving a discretization of numerical features. Comparing it with the rough set based approaches, our proposed method improves accuracy over various experimental data sets. Experimental results illustrate that much valuable information can be extracted by using this idea. This proposed technique is demonstrated on discrete, continuous, and heterogeneous data, and is compared with other FS methods in terms of subset size and classification accuracy.
dc.identifier.citationWalailak Journal of Science and Technology, 14(4), 275-297, 2017
dc.identifier.issn16863933
dc.identifier.other2-s2.0-85013774686
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7399
dc.sourceWalailak Journal of Science and Technology
dc.subjectBoundary region
dc.subjectClassification
dc.subjectFeature selection
dc.subjectMutual information
dc.subjectNeighborhood rough sets
dc.titleMin-uncertainty & max-certainty criteria of neighborhood rough-mutual feature selection
dc.typeArticle

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