Feature selection for neural networks through functional links found by evolutionary computation

Abstract

In this paper we describe different ways to select and transform features using evolutionary computation. The features are intended to serve as inputs to a feedforward network. The first way is the selection of features using a standard genetic algorithm, and the solution found specifies whether a certain feature should be present or not. We show that for the prediction of unemployment rates in various European countries, this is a succesfull approach. In fact, this kind of selection of features is a special case of so-called functional links. Functional links transform the input pattern space to a new pattern space. As functional links one can use polynomials, or more general functions. Both can be found using evolutionary computation. Polynomial functional links are found by evolving a coding of the powers of the polynomial. For symbolic functions we can use genetic programming. Genetic programming finds the symbolic functions that are to be applied to the inputs. We compare the workings of the latter two methods on two artificial datasets, and on a real-world medical image dataset.