Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection

Abstract

This article provides an optimisation method using a Genetic Algorithm approach to apply feature selection techniques for large data sets to improve accuracy. This is achieved through improved classification, a reduced number of features, and furthermore it aids in interpreting the model. A clinical dataset, based on heart failure, is used to illustrate the nature of the problem and to show the effectiveness of the techniques developed. Clinical datasets are sometimes characterised as having many variables. For instance, blood biochemistry data has more than 60 variables that have led to complexities in developing predictions of outcomes using machine-learning and other algorithms. Hence, techniques to make them more tractable are required. Genetic Algorithms can provide an efficient and low numerically complex method for effectively selecting features. In this paper, a way to estimate the number of required variables is presented, and a genetic algorithm is used in a “wrapper” form to select features for a case study of heart failure data. Additionally, different initial populations and termination conditions are used to arrive at a set of optimal features, and these are then compared with the features obtained using traditional methodologies. The paper provides a framework for estimating the number of variables and generations required for a suitable solution.