NDLI: Independence-Based MAP for Markov Networks Structure Discovery

Content Provider	IEEE Xplore Digital Library
Author	Bromberg, F. Schluter, F. Edera, A.
Copyright Year	2011
Abstract	This work presents IBMAP, an approach for robust learning of Markov network structures from data, together with IBMAP-HC, an efficient instantiation of the approach. Existing Score-Based (SB) and Independence-Based (IB) approaches must make concessions either on robustness or efficiency. IBMAP-HC improves robustness efficiently through an IB-SB hybrid approach based on the probabilistic Maximum-A-Posteriori (MAP) technique, and the IB-score, a tractable expression for computing posterior probabilities of Markov network structures. Performance is first tested against IB and SB competitors on synthetic datasets. Against IB competitors (GSMN algorithm and a version of the HHC algorithm adapted here for Markov networks discovery), IBMAP-HC showed reductions in edges Hamming distance with same order running times. Against SB competitors, both IBMAP-HC and our adaptation of HHC produced comparable Hamming distances, but with running times orders of magnitude faster. We also evaluated IBMAP-HC in a realistic, challenging test-bed: EDAs, evolutionary algorithms for optimization that estimate a distribution on each generation. Using IBMAP-HC to estimate distributions, EDAs converged to the optimum faster in all benchmark functions considered, reducing required fitness evaluations by up to 80%.
Starting Page	497
Ending Page	504
File Size	239250
Page Count	8
File Format	PDF
ISBN	9781457720680
ISSN	10823409
e-ISBN	9780769545967
DOI	10.1109/ICTAI.2011.81
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields High definition video Runtime Approximation methods Robustness Manganese Estimation of Distribution algorithms Graphical Models Markov networks Structure Learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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