An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

Karshenas, Hossein; Larrañaga Múgica, Pedro; Zhang, Qingfu y Bielza, Concha (2012). An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables. Monografía (Informe Técnico). Facultad de Informática (UPM) [antigua denominación].

Descripción

Título: An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables
Autor/es:
  • Karshenas, Hossein
  • Larrañaga Múgica, Pedro
  • Zhang, Qingfu
  • Bielza, Concha
Tipo de Documento: Monográfico (Informes, Documentos de trabajo, etc.) (Informe Técnico)
Fecha: Diciembre 2012
Materias:
Palabras Clave Informales: Feature subset selection, Multiobjective optimization, Estimation of distribution algorithm, Joint objectivevariable probabilistic modeling, Noise handling
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Ninguna

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) (An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables (Tecn. Rep by memebers of the Computational Intelligence Group -UPM)) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (599kB) | Vista Previa

Resumen

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Más información

ID de Registro: 14320
Identificador DC: http://oa.upm.es/14320/
Identificador OAI: oai:oa.upm.es:14320
Depositado por: Catedrática de Universidad Concha Bielza Lozoya
Depositado el: 21 Ene 2013 08:21
Ultima Modificación: 21 Abr 2016 13:56
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM