RDE: a novel approach to improve the classification performance and expressivity of KDB

Lou, Hua, Wang, LiMin, Duan, DingBo, Yang, Cheng and Mammadov, Musa 2018, RDE: a novel approach to improve the classification performance and expressivity of KDB, PLoS one, vol. 13, no. 7, pp. 1-18, doi: 10.1371/journal.pone.0199822.

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Title RDE: a novel approach to improve the classification performance and expressivity of KDB
Author(s) Lou, Hua
Wang, LiMin
Duan, DingBo
Yang, Cheng
Mammadov, Musa
Journal name PLoS one
Volume number 13
Issue number 7
Article ID e0199822
Start page 1
End page 18
Total pages 18
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2018-07-23
ISSN 1932-6203
Keyword(s) Bayes Theorem
Data Mining
Machine Learning
Science & Technology
Summary Bayesian network classifiers (BNCs) have demonstrated competitive classification performance in a variety of real-world applications. A highly scalable BNC with high expressivity is extremely desirable. This paper proposes Redundant Dependence Elimination (RDE) for improving the classification performance and expressivity of k-dependence Bayesian classifier (KDB). To demonstrate the unique characteristics of each case, RDE identifies redundant conditional dependencies and then substitute/remove them. The learned personalized k-dependence Bayesian Classifier (PKDB) can achieve high-confidence conditional probabilities, and graphically interpret the dependency relationships between attributes. Two thyroid cancer datasets and four other cancer datasets from the UCI machine learning repository are selected for our experimental study. The experimental results prove the effectiveness of the proposed algorithm in terms of zero-one loss, bias, variance and AUC.
Language eng
DOI 10.1371/journal.pone.0199822
Field of Research MD Multidisciplinary
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2018, Lou et al.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30119689

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