Dimensionality Reduction for Imbalanced Learning

One of the most successful data preprocessing techniques used is the reduction of the data dimensionality by means of feature selection and/or feature extraction. The key idea is to simplify the data by replacing the original features with new created that extract the main information or simply select a subset of original set. Although this topic has been carefully studied in the specialized literature for the classical predictive problems, there are also several approaches specifically devised to deal with imbalance learning scenarios. Again, their main purpose is to exploit the most informative features to preserve as much as possible the concept related to the minority class. This chapter will describe the most-known techniques of feature selection and feature extraction developed to tackle imbalance data sets. We will consider these two main families of techniques separately and we will also provide the recent advances in feature selection and feature extraction by non-linear methods. In addition, we will mention a recently proposed discretization approach which is able to reduce the numeric features into categories. The chapter is organized as follows. After a short introduction in Sect. 9.1, we will review in Sect. 9.2 the straightforward solutions devised in feature selection for tackling imbalanced classification. Next, we will delve deeper into describing more advanced techniques for feature selection in Sect. 9.3. Section 9.4 will be devoted to explain the redefined feature extraction techniques based on linear models. In Sects. 9.5 and 9.6, a non-linear feature extraction technique based on autoencoders and a discretization method will be outlined, respectively. Finally, Sect. 9.7 will conclude this chapter.