ADR-Miner: An ant-based data reduction algorithm for classification

Abstract

Classification is a central problem in the fields of data mining and machine learning. Using a training set of labelled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more effective classifiers. Two widely applied data preparation methods are feature selection and instance selection, which fall under the umbrella of data reduction. In this paper, we introduce ADR-Miner, a novel data reduction algorithm that utilizes ant colony optimization (ACO). ADR-Miner is designed to perform instance selection to improve the predictive effectiveness of the constructed classification models. Empirical evaluations on 20 benchmark data sets with three well-known classification algorithms show that ADR-Miner improves the predictive quality of the produced classifiers. The non-parametric Wilcoxon signed-ranks test is employed to determine statistical significance.