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Author Li, Chaoqun ♦ Jiang, Liangxiao ♦ Li, Hongwei
Source SpringerLink
Content type Text
Publisher Higher Education Press
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword value difference metric ♦ instance weighting ♦ naive Bayes ♦ distance-based learning algorithms ♦ Computer Science
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.
ISSN 20952228
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-23
Publisher Institution Chinese Universities
Publisher Place Heidelberg
e-ISSN 20952236
Journal Frontiers of Computer Science in China
Volume Number 8
Issue Number 2
Page Count 10
Starting Page 255
Ending Page 264

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Source: SpringerLink