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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2019

13.06.2018 | Regular Paper

Two improved attribute weighting schemes for value difference metric

verfasst von: Liangxiao Jiang, Chaoqun Li

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2019

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Abstract

Due to its simplicity, efficiency and efficacy, value difference metric (VDM) has continued to perform well against more sophisticated newcomers and thus has remained of great interest to the distance metric learning community. Of numerous approaches to improving VDM by weakening its attribute independence assumption, attribute weighting has received less attention (only two attribute weighting schemes) but demonstrated remarkable class probability estimation performance. Among two existing attribute weighting schemes, one is non-symmetric and the other is symmetric. In this paper, we propose two simple improvements for setting attribute weights for use with VDM. One is the non-symmetric Kullback–Leibler divergence weighted value difference metric (KLD-VDM) and the other is the symmetric gain ratio weighted value difference metric (GR-VDM). We performed extensive evaluations on a large number of datasets and found that KLD-VDM and GR-VDM significantly outperform two existing attribute weighting schemes in terms of the negative conditional log likelihood and root relative squared error, yet at the same time maintain the computational simplicity and robustness that characterize VDM.
Vorheriger Artikel An approach based on mixed hierarchical clustering and optimization for graph analysis in social media network: toward globally hierarchical community structure
Nächster Artikel Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction

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Metadaten
Titel
Two improved attribute weighting schemes for value difference metric
verfasst von
Liangxiao Jiang
Chaoqun Li
Publikationsdatum
13.06.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1229-3

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