Hybridised Genetic Algorithm and K-Nearest Neighbour for Rainfall Prediction

This project evaluates a hybridised k-Nearest Neighbour (k-NN) and Genetic Algorithms (GA) classifier on Melbourne rainfall prediction. K-NN is a traditional classifier that uses a set of attributes for classification. In the past, these attributes were unweighted (i.e. they all had the same relevance for classification) since the search space for finding attribute weights is too large in practice. As redundant attributes may cause a misleading distance measure for a k-NN, an untrained k-NN becomes less competitive with the other pattern recognition approaches which involve training phases. Recent research demonstrated that GAs can effectively find a promising optimal solution for combinatorial optimisation problems in an acceptably short period of time. [Kelly and Davis 91a] applied a GA to find the optimal set of attribute weights for k-NN and found it successfully improved the classification performance. The goals of this project are to produce a more effective hybridised k-NN and..