Feature Subset Selection by Estimation of Distribution Algorithms

2002 ◽  
pp. 269-293 ◽  
Author(s):  
I. Inza ◽  
P. Larrañaga ◽  
B. Sierra
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

Estimation of Distribution Algorithms for Feature Subset Selection in Large Dimensionality Domains

Data Mining ◽  
2011 ◽  
pp. 97-116 ◽  
Author(s):  
Inaki Inza ◽  
Pedro Larranaga ◽  
Basilio Sierra
Keyword(s):  
Probabilistic Models ◽  
Subset Selection ◽  
Population Based ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Estimation Of Distribution Algorithms ◽  
Text Learning ◽  
Estimation Of Distribution ◽  
Selection Tasks ◽  
Distribution Algorithms

Feature Subset Selection (FSS) is a well-known task of Machine Learning, Data Mining, Pattern Recognition or Text Learning paradigms. Genetic Algorithms (GAs) are possibly the most commonly used algorithms for Feature Subset Selection tasks. Although the FSS literature contains many papers, few of them tackle the task of FSS in domains with more than 50 features. In this chapter we present a novel search heuristic paradigm, called Estimation of Distribution Algorithms (EDAs), as an alternative to GAs, to perform a population-based and randomized search in datasets of a large dimensionality. The EDA paradigm avoids the use of genetic crossover and mutation operators to evolve the populations. In absence of these operators, the evolution is guaranteed by the factorization of the probability distribution of the best solutions found in a generation of the search and the subsequent simulation of this distribution to obtain a new pool of solutions. In this chapter we present four different probabilistic models to perform this factorization. In a comparison with two types of GAs in natural and artificial datasets of a large dimensionality, EDAbased approaches obtain encouraging results with regard to accuracy, and a fewer number of evaluations were needed than used in genetic approaches.

Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms

2015 ◽  
Vol 157 ◽  
pp. 46-60 ◽  
Author(s):  
Iñigo Mendialdua ◽  
Andoni Arruti ◽  
Ekaitz Jauregi ◽  
Elena Lazkano ◽  
Basilio Sierra
Keyword(s):  
Subset Selection ◽  
Estimation Of Distribution Algorithms ◽  
Estimation Of Distribution ◽  
Distribution Algorithms

GENE SELECTION FOR CANCER CLASSIFICATION USING WRAPPER APPROACHES

2004 ◽  
Vol 18 (08) ◽  
pp. 1373-1390 ◽  
Author(s):  
ROSA BLANCO ◽  
PEDRO LARRAÑAGA ◽  
IÑAKI INZA ◽  
BASILIO SIERRA
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
Cancer Classification ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Selection Algorithms ◽  
Selection Of ◽  
General Method

Despite the fact that cancer classification has considerably improved, nowadays a general method that classifies known types of cancer has not yet been developed. In this work, we propose the use of supervised classification techniques, coupled with feature subset selection algorithms, to automatically perform this classification in gene expression datasets. Due to the large number of features of gene expression datasets, the search of a highly accurate combination of features is done by means of the new Estimation of Distribution Algorithms paradigm. In order to assess the accuracy level of the proposed approach, the naïve-Bayes classification algorithm is employed in a wrapper form. Promising results are achieved, in addition to a considerable reduction in the number of genes. Stating the optimal selection of genes as a search task, an automatic and robust choice in the genes finally selected is performed, in contrast to previous works that research the same types of problems.

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