  DSS serve the management, operations, and planning levels of an organization
and help to make decisions, which may be rapidly changing and not easily
specified in advance. Data mining has a vital role to extract important
information to help in decision making of a decision support system.
Integration of data mining and decision support systems (DSS) can lead to the
improved 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 locking
automobile brakes to these days interactive search engines. It provides various
machine learning techniques to support data mining. The classification is one
of the main and valuable tasks of data mining. Several types of classification
algorithms have been suggested, tested and compared to determine the future
trends based on unseen data. There has been no single algorithm found to be
superior over all others for all data sets. The objective of this paper is to
compare various classification algorithms that have been frequently used in
data mining for decision support systems. Three decision trees based
algorithms, one artificial neural network, one statistical, one support vector
machines with and without ada boost and one clustering algorithm are tested and
compared on four data sets from different domains in terms of predictive
accuracy, error rate, classification index, comprehensibility and training
time. Experimental results demonstrate that Genetic Algorithm (GA) and support
vector machines based algorithms are better in terms of predictive accuracy.
SVM without adaboost shall be the first choice in context of speed and
predictive accuracy. Adaboost improves the accuracy of SVM but on the cost of
large training time.
