InstanceRank based on borders for instance selection

Instance selection algorithms are used for reducing the number of training instances. However, most of them suffer from long runtimes which results in the incapability to be used with large datasets. In this work, we introduce an Instance Ranking per class using Borders (instances near to instances belonging to different classes), using this ranking we propose an instance selection algorithm (IRB). We evaluated the proposed algorithm using k-NN with small and large datasets, comparing it against state of the art instance selection algorithms. In our experiments, for large datasets IRB has the best compromise between time and accuracy. We also tested our algorithm using SVM, LWLR and C4.5 classifiers, in all cases the selection computed by our algorithm obtained the best accuracies in average.

► Most of the instance selection algorithms suffer from long runtimes.
► We introduce an Instance Ranking per class using border class instances.
► Using this ranking we propose IRB, an instance selection algorithm.
► For large datasets IRB obtains the best compromise between time and accuracy.