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.