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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2017

25.05.2016 | Regular Paper

Toward value difference metric with attribute weighting

verfasst von: Chaoqun Li, Liangxiao Jiang, Hongwei Li, Jia Wu, Peng Zhang

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2017

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Abstract

In distance metric learning, recent work has shown that value difference metric (VDM) with a strong attribute independence assumption outperforms other existing distance metrics. However, an open question is whether VDM with a less restrictive assumption can perform even better. Many approaches have been proposed to improve VDM by weakening the assumption. In this paper, we make a comprehensive survey on the existing improved approaches and then propose a new approach to improve VDM by attribute weighting. We name the proposed new distance function as attribute-weighted value difference metric (AWVDM). Moreover, we propose a modified attribute-weighted value difference metric (MAWVDM) by incorporating the learned attribute weights into the conditional probability estimates of AWVDM. AWVDM and MAWVDM significantly outperform VDM and inherit the computational simplicity of VDM simultaneously. Experimental results on a large number of UCI data sets validate the performance of AWVDM and MAWVDM.
Vorheriger Artikel An effective and interpretable method for document classification
Nächster Artikel DBMUTE: density-based majority under-sampling technique

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Metadaten
Titel
Toward value difference metric with attribute weighting
verfasst von
Chaoqun Li
Liangxiao Jiang
Hongwei Li
Jia Wu
Peng Zhang
Publikationsdatum
25.05.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-0960-x

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