Feature subset selection is an important problem in knowledge discovery, not only for the insight gained from determining relevant modeling variables, but also for the improved understandability, scalability, and, possibly, accuracy of the resulting models. The purpose of this chapter is to provide a comprehensive analysis of feature selection via evolutionary search in supervised and unsupervised learning. To achieve this purpose, we first discuss a general framework for feature selection based on a new search algorithm, Evolutionary Local Selection Algorithm (ELSA). The search is formulated as a multi-objective optimization problem to examine the trade-off between the complexity of the generated solutions against their quality. ELSA considers multiple objectives efficiently while avoiding computationally expensive global comparison. We combine ELSA with Artificial Neural Networks (ANNs) and Expectation-Maximization (EM) algorithms for feature selection in supervised and unsupervised learning respectively. Further, we provide a new two-level evolutionary algorithm, Meta-Evolutionary Ensembles (MEE), where feature selection is used to promote the diversity among classifiers in the same ensemble.
Supervised Feature Selection With Orthogonal Regression and Feature Weighting
