Automatic Feature Selection in Neuroevolution

Abstract

Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make current machine learning methods much more useful. Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when labeled training data is available. This paper presents a novel method called FS-NEAT which extends the NEAT neuroevolution method to automatically determine the right set of inputs for the networks it evolves. By learning the network's inputs, topology, and weights simultaneously, FS-NEAT addresses the feature selection problem without relying on meta-learning or labeled data. Initial experiments in a line orientation task demonstrate that FS-NEAT can learn networks with fewer inputs and better performance than traditional NEAT. Furthermore, it outperforms traditional NEAT even when the feature set does not contain extraneous features because it searches for networks in a lower-dimensional space.