Trying to understand how analogical classifiers work

Correa, WF; Prade, H; Richard, G

Trying to understand how analogical classifiers work

Correa, WF Prade, H

Richard, G

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7520 LNAI pp. 582 - 589
Issue Date:: 2012-10-23

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Field	Value	Language
dc.contributor.author	Correa, WF	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.contributor.author	Richard, G	en_US
dc.date.issued	2012-10-23	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7520 LNAI pp. 582 - 589	en_US
dc.identifier.isbn	9783642333613	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32909
dc.description.abstract	Based on a formal modeling of analogical proportions, a new type of classifier has started to be investigated in the last past years. With such classifiers, there is no standard statistical counting or distance evaluation. Despite their differences with classical approaches, such as naive Bayesian, k-NN, or even SVM classifiers, the analogy-based classifiers appear to be quite successful. Even if this success may not come as a complete surprise, since one may imagine that a general regularity or conformity principle is still at work (as in the other classifiers), no formal explanation had been provided until now. In this research note, we lay bare the way analogy-based classifiers implement this core principle, highlighting the fact that they mainly relate changes in feature values to changes in classes. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-33362-0_46	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Trying to understand how analogical classifiers work	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7520 LNAI	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7520 LNAI	en_US

Abstract:

Based on a formal modeling of analogical proportions, a new type of classifier has started to be investigated in the last past years. With such classifiers, there is no standard statistical counting or distance evaluation. Despite their differences with classical approaches, such as naive Bayesian, k-NN, or even SVM classifiers, the analogy-based classifiers appear to be quite successful. Even if this success may not come as a complete surprise, since one may imagine that a general regularity or conformity principle is still at work (as in the other classifiers), no formal explanation had been provided until now. In this research note, we lay bare the way analogy-based classifiers implement this core principle, highlighting the fact that they mainly relate changes in feature values to changes in classes. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/32909