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has been cited by the following article:

• TITLE: Accuracies and Training Times of Data Mining Classification Algorithms: An Empirical Comparative Study

  AUTHORS: S. Olalekan Akinola, O. Jephthar Oyabugbe

  KEYWORDS: Artificial Neural Network, Classification, Data Mining, Decision Tree, Naïve Bayesian, Performance Evaluation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.8 No.9, September 16, 2015

  ABSTRACT: Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/prediction tasks in data mining. Empirical studies on these performance indicators in data mining are few. Therefore, this study was designed to determine how data mining classification algorithm perform with increase in input data sizes. Three data mining classification algorithms—Decision Tree, Multi-Layer Perceptron (MLP) Neural Network and Naïve Bayes— were subjected to varying simulated data sizes. The time taken by the algorithms for trainings and accuracies of their classifications were analyzed for the different data sizes. Results show that Naïve Bayes takes least time to train data but with least accuracy as compared to MLP and Decision Tree algorithms.