Combining argumentation and clustering techniques in pattern classification problems

Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied to determine to which cluster ci it belongs and the label of the cluster is check...

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Detalles Bibliográficos
Autores principales: Gómez, Sergio Alejandro, Chesñevar, Carlos Iván
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2003
Materias:
Ciencias Informáticas
Intelligent agents
ARTIFICIAL INTELLIGENCE
Neural nets
Clustering
Machine Learning
Defeasible Argumentation
Neural networks
Fuzzy Adaptive Resonance Theory
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22714
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22714
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Intelligent agents
ARTIFICIAL INTELLIGENCE
Neural nets
Clustering
Machine Learning
Defeasible Argumentation
Neural networks
Fuzzy Adaptive Resonance Theory
spellingShingle Ciencias Informáticas
Intelligent agents
ARTIFICIAL INTELLIGENCE
Neural nets
Clustering
Machine Learning
Defeasible Argumentation
Neural networks
Fuzzy Adaptive Resonance Theory
Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
Combining argumentation and clustering techniques in pattern classification problems
topic_facet Ciencias Informáticas
Intelligent agents
ARTIFICIAL INTELLIGENCE
Neural nets
Clustering
Machine Learning
Defeasible Argumentation
Neural networks
Fuzzy Adaptive Resonance Theory
description Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied to determine to which cluster ci it belongs and the label of the cluster is checked. In such a setting clusters can overlap, and a new instance (or example) can be assigned to more than one cluster. In many cases, determining to which cluster this new instance actually belongs requires a qualitative analysis rather than a numerical one. In this paper we present a novel approach to solve this problem by combining defeasible argumentation and a clustering algorithm based on the Fuzzy Adaptive Resonance Theory neural network model. The proposed approach takes as input a clustering algorithm and a background theory. Given a previously unseen instance enew, it will be classified using the clustering algorithm. If a conflicting situation arises, argumentation will be used in order to consider the user’s preference criteria for classifying examples.
format Objeto de conferencia
Objeto de conferencia
author Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
author_facet Gómez, Sergio Alejandro
Chesñevar, Carlos Iván
author_sort Gómez, Sergio Alejandro
title Combining argumentation and clustering techniques in pattern classification problems
title_short Combining argumentation and clustering techniques in pattern classification problems
title_full Combining argumentation and clustering techniques in pattern classification problems
title_fullStr Combining argumentation and clustering techniques in pattern classification problems
title_full_unstemmed Combining argumentation and clustering techniques in pattern classification problems
title_sort combining argumentation and clustering techniques in pattern classification problems
publishDate 2003
url http://sedici.unlp.edu.ar/handle/10915/22714
work_keys_str_mv AT gomezsergioalejandro combiningargumentationandclusteringtechniquesinpatternclassificationproblems
AT chesnevarcarlosivan combiningargumentationandclusteringtechniquesinpatternclassificationproblems
bdutipo_str Repositorios
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