Computer-based medical systems play a very important role in medical applications because they can strongly support the physicians in the decision making process. 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, achieving larger performances than a baseline classifier.
['Chongsheng Zhang', "Roberto D'Ambrosio", 'Paolo Soda']
Real-Time Biomedical Instance Selection