“Real-time” Instance Selection for Biomedical Data Classification

Abstract

Computer-based medical systems play a very important role in medical applications because they can strongly support the physicians in the decision making process. Several existing methods infer a classification function from labeled training data. The large amount of data nowadays available, although collected from high quality sources, usually contain irrelevant, redundant, or noisy information, suggesting that not all the training instances are useful for the classification task. To address this issue, we present here an instance selection method that, different from the existing approaches, selects in “real-time” a subset of instances from the original training set on the basis of the information derived from each test instance to be classified. We apply our method to seven public benchmark datasets, showing that the recognition performances are improved. We will also discuss how method parameters affect the experimental results.