  For classification problems, feature extraction is a crucial process which
aims to find a suitable data representation that increases the performance of
the machine learning algorithm. According to the curse of dimensionality
theorem, the number of samples needed for a classification task increases
exponentially as the number of dimensions (variables, features) increases. On
the other hand, it is costly to collect, store and process data. Moreover,
irrelevant and redundant features might hinder classifier performance. In
exploratory analysis settings, high dimensionality prevents the users from
exploring the data visually. Feature extraction is a two-step process: feature
construction and feature selection. Feature construction creates new features
based on the original features and feature selection is the process of
selecting the best features as in filter, wrapper and embedded methods.
  In this work, we focus on feature construction methods that aim to decrease
data dimensionality for visualization tasks. Various linear (such as principal
components analysis (PCA), multiple discriminants analysis (MDA), exploratory
projection pursuit) and non-linear (such as multidimensional scaling (MDS),
manifold learning, kernel PCA/LDA, evolutionary constructive induction)
techniques have been proposed for dimensionality reduction. Our algorithm is an
adaptive feature extraction method which consists of evolutionary constructive
induction for feature construction and a hybrid filter/wrapper method for
feature selection.
