Publication: Rough-mutual feature selection based-on minimal-boundary and maximal-lower
dc.contributor.author | Foithong, Sombut | |
dc.contributor.author | Srinil, Phaitoon | |
dc.contributor.author | Yangyuen, Kattiya Tawsopar | |
dc.contributor.author | Phattaraworamet, Thanawat | |
dc.date.accessioned | 2023-12-16T10:57:27Z | |
dc.date.available | 2023-12-16T10:57:27Z | |
dc.date.issued | 2017 | |
dc.date.issuedBE | 2560 | |
dc.description.abstract | Feature 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.doi | 10.1109/MITICON.2016.8025230 | |
dc.identifier.uri | https://harrt.in.th/handle/123456789/8558 | |
dc.language.iso | en | |
dc.publisher | IEEE Xplore | |
dc.subject | การเลือกฟีเจอร์ | |
dc.subject | สารสนเทศร่วม | |
dc.subject | การทำเหมืองข้อมูล | |
dc.subject | Feature Selection | |
dc.subject | Rough-Mutual Feature Selection | |
dc.subject | Mutual Information | |
dc.subject.isced | 0322 บรรณารักษ์, สารสนเทศ และการศึกษาจดหมายเหตุ | |
dc.subject.oecd | 5.8 นิเทศศาสตร์และสื่อสารมวลชน | |
dc.title | Rough-mutual feature selection based-on minimal-boundary and maximal-lower | |
dc.type | เอกสารตีพิมพ์ในการประชุม (Conference Proceedings) | |
dspace.entity.type | Publication | |
harrt.researchArea | สารสนเทศศาสตร์ | |
harrt.researchGroup | บรรณารักษศาสตร์และสารสนเทศศาสตร์ | |
harrt.researchTheme.1 | Data Science | |
harrt.researchTheme.2 | Machine Learning | |
harrt.researchTheme.3 | Data Mining | |
mods.location.url | https://ieeexplore.ieee.org/document/8025230 | |
oaire.citation.endPage | 137-MIT-141 | |
oaire.citation.startPage | MIT | |
oaire.citation.title | 2016 Management and Innovation Technology International Conference (MITicon) | |
oairecerif.author.affiliation | มหาวิทยาลัยบูรพา. คณะวิทยาศาสตร์และศิลปศาสตร์. การจัดการโลจิสติกส์และการค้าชายแดน | |
oairecerif.event.name | 2016 Management and Innovation Technology International Conference (MITicon) |