Abstract
Classification is the preliminary stage of data mining which is used to categorize the dataset in smaller groups where each group contains the similar data items. The classification basically deals with two main parameters; one is the number of classes and another is the criteria for deciding the class members. Different recognition algorithms also use the classification process as an initial stage to perk up the efficiency and the accuracy. The accuracy of the classification algorithm also decides the effectiveness of its use in other mining applications. The present work is about to analyze the effectiveness of the most popular classification techniques. In this paper, the analysis has been performed for five different classification algorithms in terms of accuracy, kappa statistics, execution time, mean absolute error under three datasets, collected from medical domain. The work has been implemented in WEKA environment and obtained results show that SVM is the most robust classification method and KNN is the least effective classifier for medical data sets.
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