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Conference Paper: Feature weighting by RELIEF based on local hyperplane approximation
Title | Feature weighting by RELIEF based on local hyperplane approximation |
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Authors | |
Keywords | RELIEF local hyperplane KNN Feature weighting Classification |
Issue Date | 2012 |
Publisher | Springer. |
Citation | 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2012), Kuala Lumpur, Malaysia, 29 May - 1June 2012. In Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012: Proceedings, Part II, 2012, p. 335-346 How to Cite? |
Abstract | In this paper, we propose a new feature weighting algorithm through the classical RELIEF framework. The key idea is to estimate the feature weights through local approximation rather than global measurement, as used in previous methods. The weights obtained by our method are more robust to degradation of noisy features, even when the number of dimensions is huge. To demonstrate the performance of our method, we conduct experiments on classification by combining hyperplane KNN model (HKNN) and the proposed feature weight scheme. Empirical study on both synthetic and real-world data sets demonstrate the superior performance of the feature selection for supervised learning, and the effectiveness of our algorithm. © 2012 Springer-Verlag. |
Persistent Identifier | http://hdl.handle.net/10722/276919 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
Series/Report no. | Lecture Notes in Computer Science ; 7302 |
DC Field | Value | Language |
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dc.contributor.author | Cai, Hongmin | - |
dc.contributor.author | Ng, Michael | - |
dc.date.accessioned | 2019-09-18T08:35:03Z | - |
dc.date.available | 2019-09-18T08:35:03Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2012), Kuala Lumpur, Malaysia, 29 May - 1June 2012. In Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012: Proceedings, Part II, 2012, p. 335-346 | - |
dc.identifier.isbn | 9783642302190 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276919 | - |
dc.description.abstract | In this paper, we propose a new feature weighting algorithm through the classical RELIEF framework. The key idea is to estimate the feature weights through local approximation rather than global measurement, as used in previous methods. The weights obtained by our method are more robust to degradation of noisy features, even when the number of dimensions is huge. To demonstrate the performance of our method, we conduct experiments on classification by combining hyperplane KNN model (HKNN) and the proposed feature weight scheme. Empirical study on both synthetic and real-world data sets demonstrate the superior performance of the feature selection for supervised learning, and the effectiveness of our algorithm. © 2012 Springer-Verlag. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012: Proceedings, Part II | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; 7302 | - |
dc.subject | RELIEF | - |
dc.subject | local hyperplane | - |
dc.subject | KNN | - |
dc.subject | Feature weighting | - |
dc.subject | Classification | - |
dc.title | Feature weighting by RELIEF based on local hyperplane approximation | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-642-30220-6_28 | - |
dc.identifier.scopus | eid_2-s2.0-84861444374 | - |
dc.identifier.spage | 335 | - |
dc.identifier.epage | 346 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.publisher.place | Berlin | - |
dc.identifier.issnl | 0302-9743 | - |