The random sets approach is heuristic in nature and has been inspired by the growing speed of computations. For example, we can consider a large number of classifiers where any single classifier is based on a relatively small subset of randomly selected features or random sets of features. Using cross-validation we can rank all random sets according to the selected criterion, and use this ranking for further feature selection. Another application of random sets was motivated by the huge

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... ed data, which represent significant problem because the corresponding classifier has a tendency to ignore patterns with smaller representation in the training set. Again, we propose to consider a large number of balanced training subsets where representatives from both patterns are selected randomly. The above models demonstrated competitive results in two data mining competitions.