In other words, some algorithms may perform better than the others for some specific domain datasets, but may perform worse than others over other domain datasets. However, different algorithms tend to use different criteria to determine the noisy data, making it difficult to find the best algorithm for different domain datasets. In the literature, many instance selection algorithms have been proposed. In addition, when the instance selection algorithm was carefully chosen, a reduction in the training set so that it contains less noisy data can usually make the classifiers perform better than the ones without considering instance selection. This reduces the size of the training set, which then requires less storage space. It focuses on selecting representative data samples from a given training set, whereas unrepresentative (or noisy) data samples are filtered out. Instance selection is an important problem in medical data mining.
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