Data Mining Strategy Selection via Empirical and Constructive Induction

M. Pechenizkiy (Finland)


Data mining, meta-learning, constructive induction


Nowadays there exist a number of data-mining techniques to extract knowledge from large databases. Recent research has shown that no single technique can dominate some other technique on all possible data-mining problems. Nevertheless, many empirical studies report that a technique or a group of techniques can perform significantly better than any other technique on a certain data-mining problem or a group of problems. Therefore, a data mining system has a challenge of selecting the most appropriate technique(s) for a problem at hand. In the real world it is infeasible to perform a comparison of all applicable approaches. Several meta-learning approaches have been applied for automatic technique selection by several researchers with little success. The goal of this paper is to consider and critically analyze such approaches. In the centre of our analysis we introduce a general framework for data mining strategy selection via empirical and constructive induction.

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