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Memorias de investigación
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An interval-based multi-objective approach to feature subset selection using joint modeling of objectives and variables
Año:2012
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
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.
Internacional
No
Entidad
Lugar
Páginas
Referencia/URL
Tipo de publicación
Technical Report
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Hossein Karshenas Najafabadi (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
  • Autor: Qingfu Zhang
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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