Comparing Fitness Functions for Genetic Feature Transformation

A representation of features is a very important parameter when creating machine learning models. The main goal of this paper is to introduce a way to compare feature space transformations that change this representation. It particularly deals with a comparison of a method that uses a genetic programming based on different fitness functions for transformation of feature space. The fitness function is a very important part of the genetic algorithm because it defines required properties of new feature space. Several possible fitness functions are described and compared in this paper. The process of the comparison is also introduced in this paper and selected functions are tested and their results are compared to each other. These results are compared and upsides and downsides of each method are discussed in conclusion. A part of the work is a framework used to automate processes needed to create this comparison.