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DSS serve the management, operations, and planning levels of an organizationand help to make decisions, which may be rapidly changing and not easilyspecified in advance. Data mining has a vital role to extract importantinformation to help in decision making of a decision support system.Integration of data mining and decision support systems (DSS) can lead to theimproved performance and can enable the tackling of new types of problems.Artificial Intelligence methods are improving the quality of decision support,and have become embedded in many applications ranges from ant lockingautomobile brakes to these days interactive search engines. It provides variousmachine learning techniques to support data mining. The classification is oneof the main and valuable tasks of data mining. Several types of classificationalgorithms have been suggested, tested and compared to determine the futuretrends based on unseen data. There has been no single algorithm found to besuperior over all others for all data sets. The objective of this paper is tocompare various classification algorithms that have been frequently used indata mining for decision support systems. Three decision trees basedalgorithms, one artificial neural network, one statistical, one support vectormachines with and without ada boost and one clustering algorithm are tested andcompared on four data sets from different domains in terms of predictiveaccuracy, error rate, classification index, comprehensibility and trainingtime. Experimental results demonstrate that Genetic Algorithm (GA) and supportvector machines based algorithms are better in terms of predictive accuracy.SVM without adaboost shall be the first choice in context of speed andpredictive accuracy. Adaboost improves the accuracy of SVM but on the cost oflarge training time.

A Benchmark to Select Data Mining Based Classification Algorithms For Business Intelligence And Decision Support Systems