On the Use of Metaheuristics for Feature Selection in Classification
Clarisse Dhaenens, Laëtitia Jourdan · 2016
This chapter explains how feature selection can be realized with metaheuristics and why metaheuristics can help realize the feature selection task in a Big Data context. Feature selection, also known as variable selection, attribute selection or variable subset selection, aims at selecting an optimum relevant set of features or attributes that are necessary for classification. Filter models select features independent of any specific classifiers. However, the major disadvantage of the filter approach is that it totally ignores the effects of the selected feature subset on the performance of the classifier. Embedded methods are similar to wrapper methods in the sense that the search of an optimal subset is performed for a specific learning algorithm, but they are characterized by a deeper interaction between feature selection and classifier construction. The binary representation has often been used because it implies simplicity in operator implementation.