Searching for feature pre-processing methods in MLP binary classification with Genetic Algorithms

Feature pre-processing is an essential step for most machine learning algorithms. Pre-processing usually involves transforming the input data to a form that is more suitable for the learning algorithm. Various techniques can be used for pre-processing, such as feature scaling, feature extraction, and feature selection which help to improve the performance of the learning algorithm. This study develops novel pre-processing methods through a genetic programming approach. Genetic Algorithms were used to search for a combination of pre-processing operations that produced the best results of Multilayer Perceptrons on a set of binary classification datasets. The search findings show that these discovered methods, when combined with existing methods, statistically outperform existing methods by themselves on new datasets. Visualization of the effects its on synthetic data show that these discovered methods extend the range of the data and direct values away from the center of the data. This study provides practitioners with new methods that can be used as pre-processing techniques for machine learning algorithms.