Authors:
Marek Kurzynski
;
Pawel Trajdos
and
Maciej Krysmann
Affiliation:
Wroclaw University of Technology, Poland
Keyword(s):
Feature Selection, Feature Relevance, Diversity of Feature Ensemble, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
In the paper the novel feature selection method, using wrapper model and ensemble approach, is presented.
In the proposed method features are selected dynamically, i.e. separately for each classified object. First,
a set of identical one-feature classifiers using different single feature is created and next the ensemble of
features (classifiers) is selected as a solution of optimization problem using genetic algorithm. As an optimality
criterion, the sum of measures of features relevance and diversity of ensemble of features is adopted. Both
measures are calculated using original concept of randomized reference classifier, which on average acts like
classifier with evaluated feature. The performance of the proposed method was compared against six state-of-
art feature selection methods using nine benchmark databases. The experimental results clearly show the
effectiveness of the dynamic mode and ensemble approach in feature selection procedure.