NDLI: Learning Bayesian networks from incomplete data based on EMI method

Content Provider	IEEE Xplore Digital Library
Author	Fengzhan Tian Hongwei Zhang Yuchang Lu
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China (Fengzhan Tian; Hongwei Zhang; Yuchang Lu)
Abstract	Currently, there are few efficient methods in practice for learning Bayesian networks from incomplete data, which affects their use in real world data mining applications. We present a general-duty method that estimates the (conditional) mutual information directly from incomplete datasets, EMI. EMI starts by computing the interval estimates of a joint probability of a variable set, which are obtained from the possible completions of the incomplete dataset. And then computes a point estimate via a convex combination of the extreme points, with weights depending on the assumed pattern of missing data. Finally, based on these point estimates, EMI gets the estimated (conditional) mutual information. We also apply EMI to the dependency analysis based learning algorithm by J. Cheng so as to efficiently learn BNs with incomplete data. The experimental results on Asia and Alarm networks show that EMI based algorithm is much more efficient than two search & scoring based algorithms, SEM and EM-EA algorithms. In terms of accuracy, EMI based algorithm is more accurate than SEM algorithm, and comparable with EM-EA algorithm.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence IEEE Comput. Soc. Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	323
Ending Page	330
File Size	319248
Page Count	8
File Format	PDF
ISBN	0769519784
DOI	10.1109/ICDM.2003.1250936
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Electromagnetic interference Computer science Convergence Data mining Mutual information Probability distribution Sampling methods Application software Algorithm design and analysis
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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