Feature extraction for the k-nearest neighbour classifier with genetic programming

Abstract

In pattern recognition the curse of dimensionality can be handled either by reducing the number of features, e.g. with decision trees or by extraction of new features. We propose a genetic programming (GP) framework for automatic ex- traction of features with the express aim of dimension reduction and the additional aim of improving accuracy of the k-nearest neighbour (k- NN) classifier. We will show that our system is capable of reducing most datasets to one or two features while k-NN accuracy improves or stays the same. Such a small number of features has the great advantage of allowing visual inspection of the dataset in a two-dimensional plot. Since k-NN is a non-linear classification algorithm[2], we compare several linear fitness measures. We will show the a very simple one, the accuracy of the minimal distance to means (mdm) classifier outperforms all other fitness measures. We introduce a stopping criterion gleaned from numeric mathematics. New features are only added if the relative increase in training accuracy is more than a constant d, for the mdm classifier estimated to be 3.3%.