A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems

Abstract: Feature selection is the process of automatically selecting important features from data. It is an essential part of machine learning, artificial intelligence, data mining, and modelling in general. There are many feature selection algorithms available and the appropriate choice can be difficult. The aim of this thesis was to compare feature selection algorithms in order to provide an experimental basis for which algorithm to choose. The first phase involved assessing which algorithms are most common in the scientific community, through a systematic literature study in the two largest reference databases: Scopus and Web of Science. The second phase involved constructing and implementing a benchmark pipeline to compare 31 algorithms’ performance on 50 data sets.The selected features were used to construct classification models and their predictive performances were compared, as well as the runtime of the selection process. The results show a small overall superiority of embedded type algorithms, especially types that involve Decision Trees. However, there is no algorithm that is significantly superior in every case. The pipeline and data from the experiments can be used by practitioners in determining which algorithms to apply to their respective problems.