Using Prior Knowledge to Improve the Performance of an Estimation of Distribution Algorithm Applied to Feature Selection

Authors:
Leonardo R. Emmendorfer;Rodrigo Traleski;Aurora Trinidad Ramirez Pozo
Affiliations:
Engineering - UFPR;Department of Informatics - UFPR;Department of Informatics - UFPR
Venue:
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Year:
2005

Citing 0
Cited 1

Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature selection provides a great enhancement in the process of building a classifier model. A recent approach to feature selection is the use of Estimation of Distribution Algorithms (EDAs). Those algorithms's performance is greatly affected by the initial population, so prior knowledge about the problem is very important. The most important prior knowledge about the features is the relative order of importance observed among them, which can be obtained by some statistical measure. Based on the use of that kind of knowledge, some improvements are proposed and theoretically discussed. An experiment is presented, which evaluates potential benefits of those alternatives.