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Article

IBC: A First-Order Bayesian Classifier

Authors:

Peter A. Flach,

Nicolas LachicheAuthors Info & Claims

ILP '99: Proceedings of the 9th International Workshop on Inductive Logic Programming

Pages 92 - 103

Published: 24 June 1999 Publication History

Abstract

In this paper we present 1BC, a first-order Bayesian Classifier. Our approach is to view individuals as structured terms, and to distinguish between structural predicates referring to subterms (e.g. atoms from molecules), and properties applying to one or several of these subterms (e.g. a bond between two atoms). We describe an individual in terms of elementary features consisting of zero or more structural predicates and one property; these features are considered conditionally independent following the usual naive Bayes assumption. 1BC has been implemented in the context of the first-order descriptive learner Tertius, and we describe several experiments demonstrating the viability of our approach.

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Cited By

Aguwa COlya MMonplaisir L(2017)Modeling of fuzzy-based voice of customer for business decision analyticsKnowledge-Based Systems10.1016/j.knosys.2017.03.019125:C(136-145)Online publication date: 1-Jun-2017
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https://dl.acm.org/doi/10.1016/j.dss.2009.07.004
Kersting KDe Raedt L(2008)Basic principles of learning Bayesian logic programsProbabilistic inductive logic programming10.5555/1793956.1793965(189-221)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.5555/1793956.1793965
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cover image Guide Proceedings

ILP '99: Proceedings of the 9th International Workshop on Inductive Logic Programming

June 1999

297 pages

ISBN:3540661093

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 24 June 1999

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Cited By

Aguwa COlya MMonplaisir L(2017)Modeling of fuzzy-based voice of customer for business decision analyticsKnowledge-Based Systems10.1016/j.knosys.2017.03.019125:C(136-145)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.03.019
Chen HLiu HHan JYin XHe J(2009)Exploring optimization of semantic relationship graph for multi-relational Bayesian classificationDecision Support Systems10.1016/j.dss.2009.07.00448:1(112-121)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1016/j.dss.2009.07.004
Kersting KDe Raedt L(2008)Basic principles of learning Bayesian logic programsProbabilistic inductive logic programming10.5555/1793956.1793965(189-221)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.5555/1793956.1793965
Inuzuka NMotoyama JUrazawa SNakano T(2008)Relational pattern mining based on equivalent classes of properties extracted from samplesProceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining10.5555/1786574.1786631(582-591)Online publication date: 20-May-2008
https://dl.acm.org/doi/10.5555/1786574.1786631
Neville JJensen D(2007)Relational Dependency NetworksThe Journal of Machine Learning Research10.5555/1248659.12486838(653-692)Online publication date: 1-May-2007
https://dl.acm.org/doi/10.5555/1248659.1248683
Železný FLavrač N(2006)Propositionalization-based relational subgroup discovery with RSDMachine Language10.1007/s10994-006-5834-062:1-2(33-63)Online publication date: 1-Feb-2006
https://dl.acm.org/doi/10.1007/s10994-006-5834-0
Kersting K(2005)An Inductive Logic Programming Approach to Statistical Relational LearningProceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning10.5555/1565360.1565361(1-228)Online publication date: 29-May-2005
https://dl.acm.org/doi/10.5555/1565360.1565361
Liu HYin XHan JDzeroski SBlockeel H(2005)An efficient multi-relational Naïve Bayesian classifier based on semantic relationship graphProceedings of the 4th international workshop on Multi-relational mining10.1145/1090193.1090200(39-48)Online publication date: 21-Aug-2005
https://dl.acm.org/doi/10.1145/1090193.1090200
Lachiche N(2005)Good and bad practices in propositionalisationProceedings of the 9th conference on Advances in Artificial Intelligence10.1007/11558590_5(50-61)Online publication date: 21-Sep-2005
https://dl.acm.org/doi/10.1007/11558590_5
Gyftodimos EFlach P(2005)Combining bayesian networks with higher-order data representationsProceedings of the 6th international conference on Advances in Intelligent Data Analysis10.1007/11552253_14(145-156)Online publication date: 8-Sep-2005
https://dl.acm.org/doi/10.1007/11552253_14
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