文章基本信息

标题：TPDA2 ALGORITHM FOR LEARNING BN STRUCTURE FROM MISSING VALUE AND OUTLIERS IN DATA MINING
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作者：Benhard Sitohang ; G.A. Putri Saptawati
期刊名称：Jurnal Informatika
印刷版ISSN：1411-0105
出版年度：2006
卷号：7
期号：2
页码：108-113
DOI：10.9744/informatika.7.2.pp. 108-113
语种：English
出版社：Institute of Research and Community Outreach - Petra Christian University
摘要：Three-Phase Dependency Analysis (TPDA) algorithm was proved as most efficient algorithm (which requires at most O(N4) Conditional Independence (CI) tests). By integrating TPDA with "node topological sort algorithm", it can be used to learn Bayesian Network (BN) structure from missing value (named as TPDA1 algorithm). And then, outlier can be reduced by applying an "outlier detection & removal algorithm" as pre-processing for TPDA1. TPDA2 algorithm proposed consists of those ideas, outlier detection & removal, TPDA, and node topological sort node.
关键词：missing value, noisy data, BN structure, TPDA.