TPDA2 ALGORITHM FOR LEARNING BN STRUCTURE FROM MISSING VALUE AND OUTLIERS IN DATA MINING
DOI:
https://doi.org/10.9744/informatika.7.2.pp.%20108-113Keywords:
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.Downloads
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
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