%0 Journal Article
%T A Benchmark to Select Data Mining Based Classification Algorithms for Business Intelligence and Decision Support Systems
%A Pardeep Kumar
%A Nitin
%A Vivek Kumar Sehgal
%A Durg Singh Chauhan
%J International Journal of Data Mining & Knowledge Management Process
%D 2012
%I Academy & Industry Research Collaboration Center (AIRCC)
%X In today¡¯s business scenario, we percept major changes in how managers use computerized support inmaking decisions. As more number of decision-makers use computerized support in decision making,decision support systems (DSS) is developing from its starting as a personal support tool and is becomingthe common resource in an organization. DSS serve the management, operations, and planning levels of anorganization and help to make decisions, which may be rapidly changing and not easily specified inadvance. Data mining has a vital role to extract important information to help in decision making of adecision support system. It has been the active field of research in the last two-three decades. Integration ofdata mining and decision support systems (DSS) can lead to the improved performance and can enable thetackling of new types of problems. Artificial Intelligence methods are improving the quality of decisionsupport, and have become embedded in many applications ranges from ant locking automobile brakes tothese days interactive search engines. It provides various machine learning techniques to support datamining. The classification is one of the main and valuable tasks of data mining. Several types ofclassification algorithms have been suggested, tested and compared to determine the future trends based onunseen data. There has been no single algorithm found to be superior over all others for all data sets.Various issues such as predictive accuracy, training time to build the model, robustness and scalabilitymust be considered and can have tradeoffs, further complex the quest for an overall superior method. Theobjective of this paper is to compare various classification algorithms that have been frequently used indata mining for decision support systems. Three decision trees based algorithms, one artificial neuralnetwork, one statistical, one support vector machines with and without adaboost and one clusteringalgorithm are tested and compared on four datasets from different domains in terms of predictive accuracy,error rate, classification index, comprehensibility and training time. Experimental results demonstrate thatGenetic Algorithm (GA) and support vector machines based algorithms are better in terms of predictiveaccuracy. Former shows highest comprehensibility but is slower than later. From the decision tree basedalgorithms, QUEST produces trees with lesser breadth and depth showing more comprehensibility. Thisresearch work shows that GA based algorithm is more powerful algorithm and shall be the first choice oforganizations for their decision support
%K Artificial Intelligence
%K Decision Support System
%K Data Mining
%K KDD
%K Classification Algorithms
%K Predictive Accuracy
%K Comprehensibility
%K Genetic Algorithm
%U http://airccse.org/journal/ijdkp/papers/2512ijdkp03.pdf