Foundations of Feature Selection

Abstract

Abstract In order to confront the problem of the high dimensionality of data, feature selection algorithms have become indispensable components of the learning process. Therefore, a correct selection of the features can lead to an improvement of the inductive learner in terms of learning speed, generalization capacity or simplicity of the induced model. A global overview of the feature selection process is given in Section 2.1. Then, Section 2.2 describes the different types of feature selection methods, as well as providing a description of the most popular algorithms for further analysis and explanation in subsequent chapters of the book. Finally, Section 2.3 summarizes this chapter.