Publication:
Rough-mutual feature selection based-on minimal-boundary and maximal-lower

dc.contributor.authorFoithong, Sombut
dc.contributor.authorSrinil, Phaitoon
dc.contributor.authorYangyuen, Kattiya Tawsopar
dc.contributor.authorPhattaraworamet, Thanawat
dc.date.accessioned2023-12-16T10:57:27Z
dc.date.available2023-12-16T10:57:27Z
dc.date.issued2017
dc.date.issuedBE2560
dc.description.abstractFeature selection (FS) is an important preprocessing step for many applications in Data Mining. Most existing FS methods based on rough set theory focus on dependency function; which is based on lower approximation as for measuring the goodness of the feature subset. However; by determining only information from a positive region but neglecting a boundary region; mostly relevant information could be invisible. This paper; the minimal boundary region - maximal lower approximation (mBML) criterion; focuses on feature selection methods based on rough set and mutual information (MI) which use the different values among the lower approximation information and the information contained in the boundary region. The use of this criterion can result in higher predictive accuracy than those obtained using the measure based on the positive region alone. Experimental results are illustrated for crisp and real-valued data and compared with other FS methods in terms of subset size; runtime; and classification accuracy.
dc.identifier.doi10.1109/MITICON.2016.8025230
dc.identifier.urihttps://harrt.in.th/handle/123456789/8558
dc.language.isoen
dc.publisherIEEE Xplore
dc.subjectการเลือกฟีเจอร์
dc.subjectสารสนเทศร่วม
dc.subjectการทำเหมืองข้อมูล
dc.subjectFeature Selection
dc.subjectRough-Mutual Feature Selection
dc.subjectMutual Information
dc.subject.isced0322 บรรณารักษ์, สารสนเทศ และการศึกษาจดหมายเหตุ
dc.subject.oecd5.8 นิเทศศาสตร์และสื่อสารมวลชน
dc.titleRough-mutual feature selection based-on minimal-boundary and maximal-lower
dc.typeเอกสารตีพิมพ์ในการประชุม (Conference Proceedings)
dspace.entity.typePublication
harrt.researchAreaสารสนเทศศาสตร์
harrt.researchGroupบรรณารักษศาสตร์และสารสนเทศศาสตร์
harrt.researchTheme.1Data Science
harrt.researchTheme.2Machine Learning
harrt.researchTheme.3Data Mining
mods.location.urlhttps://ieeexplore.ieee.org/document/8025230
oaire.citation.endPage137-MIT-141
oaire.citation.startPageMIT
oaire.citation.title2016 Management and Innovation Technology International Conference (MITicon)
oairecerif.author.affiliationมหาวิทยาลัยบูรพา. คณะวิทยาศาสตร์และศิลปศาสตร์. การจัดการโลจิสติกส์และการค้าชายแดน
oairecerif.event.name2016 Management and Innovation Technology International Conference (MITicon)
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