Cost-sensitive classification with k-nearest neighbors

Qin, Z; Wang, AT; Zhang, C; Zhang, S

Cost-sensitive classification with k-nearest neighbors

Qin, Z Wang, AT Zhang, C

Zhang, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8041 LNAI pp. 112 - 131
Issue Date:: 2013-08-01

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Field	Value	Language
dc.contributor.author	Qin, Z	en_US
dc.contributor.author	Wang, AT	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2013-08-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8041 LNAI pp. 112 - 131	en_US
dc.identifier.isbn	9783642397868	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33945
dc.description.abstract	Cost-sensitive learning algorithms are typically motivated by imbalance data in clinical diagnosis that contains skewed class distribution. While other popular classification methods have been improved against imbalance data, it is only unsolved to extend k-Nearest Neighbors (kNN) classification, one of top-10 datamining algorithms, to make it cost-sensitive to imbalance data. To fill in this gap, in this paper we study two simple yet effective cost-sensitive kNN classification approaches, called Direct-CS-kNN and Distance-CS-kNN. In addition, we utilize several strategies (i.e., smoothing, minimum-cost k value selection, feature selection and ensemble selection) to improve the performance of Direct-CS-kNN and Distance-CS-kNN. We conduct several groups of experiments to evaluate the efficiency with UCI datasets, and demonstrate that the proposed cost-sensitive kNN classification algorithms can significantly reduce misclassification cost, often by a large margin, as well as consistently outperform CS-4.5 with/without additional enhancements. © 2013 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-39787-5-10	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Cost-sensitive classification with k-nearest neighbors	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8041 LNAI	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8041 LNAI	en_US

Abstract:

Cost-sensitive learning algorithms are typically motivated by imbalance data in clinical diagnosis that contains skewed class distribution. While other popular classification methods have been improved against imbalance data, it is only unsolved to extend k-Nearest Neighbors (kNN) classification, one of top-10 datamining algorithms, to make it cost-sensitive to imbalance data. To fill in this gap, in this paper we study two simple yet effective cost-sensitive kNN classification approaches, called Direct-CS-kNN and Distance-CS-kNN. In addition, we utilize several strategies (i.e., smoothing, minimum-cost k value selection, feature selection and ensemble selection) to improve the performance of Direct-CS-kNN and Distance-CS-kNN. We conduct several groups of experiments to evaluate the efficiency with UCI datasets, and demonstrate that the proposed cost-sensitive kNN classification algorithms can significantly reduce misclassification cost, often by a large margin, as well as consistently outperform CS-4.5 with/without additional enhancements. © 2013 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/33945