TPDA2 ALGORITHM FOR LEARNING BN STRUCTURE FROM MISSING VALUE AND OUTLIERS IN DATA MINING

Authors

  • Benhard Sitohang Software Engineering & Data Research Group, School of electrical Engineering & Informatics, ITB
  • G.A. Putri Saptawati Software Engineering & Data Research Group, School of electrical Engineering & Informatics, ITB

:

https://doi.org/10.9744/informatika.7.2.pp.%20108-113

Keywords:

missing value, noisy data, BN structure, TPDA.

Abstract

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.

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Published

2007-02-01

How to Cite

Sitohang, B., & Saptawati, G. P. (2007). TPDA2 ALGORITHM FOR LEARNING BN STRUCTURE FROM MISSING VALUE AND OUTLIERS IN DATA MINING. Jurnal Informatika, 7(2), pp. 108-113. https://doi.org/10.9744/informatika.7.2.pp. 108-113

Issue

Vol. 7 No. 2 (2006): NOVEMBER 2006

Section

Articles

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