Feature selection based on community detection in feature correlation networks

Abstract

© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. Feature selection is an important data preprocessing step in data mining and machine learning tasks, especially in the case of high dimensional data. In this paper, we propose a novel feature selection method based on feature correlation networks, i.e. complex weighted networks describing the strongest correlations among features in a dataset. The method utilizes community detection techniques to identify cohesive groups of features in feature correlation networks. A subset of features exhibiting a strong association with the class variable is selected according to the identified community structure taking into account the size of feature communities and connections within them. The proposed method is experimentally evaluated on a high dimensional dataset containing signaling protein features related to the diagnosis of Alzheimer’s disease. We compared the performance of seven commonly used classifiers that were trained without feature selection, after feature selection by four variants of our method determined by different community detection techniques, and after feature selection by four widely used state-of-the-art feature selection methods available in the WEKA machine learning library. The results of the experimental evaluation indicate that our method improves the classification accuracy of several classification models while greatly reducing the dimensionality of the dataset. Additionally, our method tends to outperform traditional feature selection methods provided by the WEKA library.