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A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset. Specifically, if much redundant and unrelated or noisy and unreliable information is presented, then knowledge discovery becomes a very difficult problem. It is well-known that data preparation steps require significant processing time in machine learning tasks. It would be very helpful and quite useful if there were various preprocessing algorithms with the same reliable and effective performance across all datasets, but this is impossible. To this end, we present the most well-known and widely used up-to-date algorithms for each step of data preprocessing in the framework of predictive data mining.

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A variety of methods have been developed in order to tackle a classication problem in the eld of decision support systems. A hybrid prediction scheme which combines several classiers, rather than selecting a single robust method, is a good alternative solution. In order to address this issue, we have provided an ensemble of classiers to create a hybrid decision support system. This method based on stacking variant methodology that combines strong ensembles to make predictions.The presented hybrid method has been compared with other knownensembles. The experiments conducted on several standard benchmark datasets showed that the proposed scheme gives promising results in terms of accuracy in most of the cases.

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