An approach to automated agent negotiation using belief revision

Feature selection is a useful machine learning technique aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. The well-known Recursive Feature Elimination (RFE) algorithm provides good...

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Detalles Bibliográficos
Autores principales: Pilotti, Pablo, Casali, Ana, Chesñevar, Carlos Iván
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2011
Materias:
Ciencias Informáticas
Approach to Automated Agent Negotiation
Belief Revision
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/125265
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-125265
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
Approach to Automated Agent Negotiation
Belief Revision
spellingShingle Ciencias Informáticas
Approach to Automated Agent Negotiation
Belief Revision
Pilotti, Pablo
Casali, Ana
Chesñevar, Carlos Iván
An approach to automated agent negotiation using belief revision
topic_facet Ciencias Informáticas
Approach to Automated Agent Negotiation
Belief Revision
description Feature selection is a useful machine learning technique aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. The well-known Recursive Feature Elimination (RFE) algorithm provides good performance with moderate computational efforts, in particular for wide datasets. When using Support Vector Machines (SVM) for multiclass classification problems, the most typical strategy is to apply a simple One–Vs–One (OVO) strategy to produce a multiclass classifier starting from binary ones. In this work we introduce improved methods to produce the final ranking of features on multiclass problems with OVO–SVM, based on different combinations of the set of rankings produced by the diverse binary problems. We evaluated our new strategies using wide datasets from mass–spectrometry analysis and standard datasets from the UCI repository. In particular, we compared the new methods with the traditional average strategy. Our results suggest that one of our new methods outperforms the traditional scheme in most situations.
format Objeto de conferencia
Objeto de conferencia
author Pilotti, Pablo
Casali, Ana
Chesñevar, Carlos Iván
author_facet Pilotti, Pablo
Casali, Ana
Chesñevar, Carlos Iván
author_sort Pilotti, Pablo
title An approach to automated agent negotiation using belief revision
title_short An approach to automated agent negotiation using belief revision
title_full An approach to automated agent negotiation using belief revision
title_fullStr An approach to automated agent negotiation using belief revision
title_full_unstemmed An approach to automated agent negotiation using belief revision
title_sort approach to automated agent negotiation using belief revision
publishDate 2011
url http://sedici.unlp.edu.ar/handle/10915/125265
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AT pilottipablo approachtoautomatedagentnegotiationusingbeliefrevision
AT casaliana approachtoautomatedagentnegotiationusingbeliefrevision
AT chesnevarcarlosivan approachtoautomatedagentnegotiationusingbeliefrevision
bdutipo_str Repositorios
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