NDLI: Naive Bayes for value difference metric

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Naive Bayes for value difference metric

Content Provider	Springer Nature Link
Author	Li, Chaoqun Jiang, Liangxiao Li, Hongwei
Copyright Year	2014
Abstract	The value difference metric (VDM) is one of the best-known and widely used distance functions for nominal attributes. This work applies the instanceweighting technique to improveVDM. An instance weighted value difference metric (IWVDM) is proposed here. Different from prior work, IWVDM uses naive Bayes (NB) to find weights for training instances. Because early work has shown that there is a close relationship between VDM and NB, some work on NB can be applied to VDM. The weight of a training instance x, that belongs to the class c, is assigned according to the difference between the estimated conditional probability ^P(c\|x) by NB and the true conditional probability P(c\|x), and the weight is adjusted iteratively. Compared with previous work, IWVDM has the advantage of reducing the time complexity of the process of finding weights, and simultaneously improving the performance of VDM. Experimental results on 36 UCI datasets validate the effectiveness of IWVDM.
Starting Page	255
Ending Page	264
Page Count	10
File Format	PDF
ISSN	20952228
Journal	Frontiers of Computer Science in China
Volume Number	8
Issue Number	2
e-ISSN	20952236
Language	English
Publisher	Higher Education Press
Publisher Date	2014-01-23
Publisher Institution	Chinese Universities
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	value difference metric instance weighting naive Bayes distance-based learning algorithms Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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