Instance Selection with Ant Colony Optimization

Abstract Classification is a supervised learning task where a training set is used to construct a classifi- cation model, which is then used to predict the class of unforeseen test instances. It is often beneficial to use only a subset of the full training set to construct the classification model, and Instance Selection is the task of selecting the most appropriate subset of the training set. In many cases, the classification model induced from the reduced training set can have bet- ter predictive accuracy on test instances. ADR-Miner is a recently introduced Ant Colony Optimization algorithm for Instance Selection that aims to produce classification models with improved test set predictive accuracy. In this paper, we present an extension of ADR-Miner, where one classification algorithm is employed in the instance selection process, and potentially a different algorithm is employed in the final model construction phase. We evaluate perfor- mance using 37 UCI datasets, and we note the combinations of algorithms which produce the best results.