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

Karshenas, Hossein and Larrañaga Múgica, Pedro and Zhang, Qingfu and Bielza, Concha (2012). An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables. Monografía (Technical Report). Facultad de Informática (UPM).

Description

Title: An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables
Author/s:
  • Karshenas, Hossein
  • Larrañaga Múgica, Pedro
  • Zhang, Qingfu
  • Bielza, Concha
Item Type: Monograph (Technical Report)
Date: December 2012
Subjects:
Freetext Keywords: Feature subset selection, Multiobjective optimization, Estimation of distribution algorithm, Joint objectivevariable probabilistic modeling, Noise handling
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: None

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Abstract

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.

More information

Item ID: 14320
DC Identifier: http://oa.upm.es/14320/
OAI Identifier: oai:oa.upm.es:14320
Deposited by: Catedrática de Universidad Concha Bielza Lozoya
Deposited on: 21 Jan 2013 08:21
Last Modified: 21 Apr 2016 13:56
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