Bayesian class-matched multinet classifier

Yaniv Gurwicz, Boaz Lerner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

A Bayesian multinet classifier allows a different set of independence assertions among variables in each of a set of local Bayesian networks composing the multinet. The structure of the local network is usually learned using a joint-probability-based score that is less specific to classification, i.e., classifiers based on structures providing high scores are not necessarily accurate. Moreover, this score is less discriminative for learning multinet classifiers because generally it is computed using only the class patterns and avoiding patterns of the other classes. We propose the Bayesian class-matched multinet (BCM2) classifier to tackle both issues. The BCM2 learns each local network using a detection-rejection measure, i.e., the accuracy in simultaneously detecting class patterns while rejecting patterns of the other classes. This classifier demonstrates superior accuracy to other state-of-the-art Bayesian network and multinet classifiers on 32 real-world databases.

Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Proceedings
PublisherSpringer Verlag
Pages145-153
Number of pages9
ISBN (Print)3540372369, 9783540372363
DOIs
StatePublished - 1 Jan 2006
EventJoint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006 - Hong Kong, China
Duration: 17 Aug 200619 Aug 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4109 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceJoint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006
Country/TerritoryChina
CityHong Kong
Period17/08/0619/08/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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