Hybridisation of Genetic Programming and Nearest Neighbour for classification

Abstract

In this paper, we propose a novel hybrid classification method which is based on two distinct approaches, namely Genetic Programming (GP) and Nearest Neighbour (kNN). The method relies on a memory list which contains some correctly labelled instances and is formed by classifiers evolved by GP. The class label of a new instance will be determined by combining its most similar instances in the memory list and the output of GP classifier on this instance. The results show that this proposed method can outperform conventional GP-based classification approach. Compared with conventional classification methods such as Naive Bayes, SVM, Decision Trees, and conventional kNN, this method can also achieve better or comparable accuracies on a set of binary problems. The evaluation cost of this hybrid method is much lower than that of conventional kNN.