There are many factors that influence classifiers behavior in machine learning, and thus determining the best classifier is not an easy task. One way of tackling this problem is by experimenting the classifiers with several performance measures. In this paper, the behaviors of machine learning classifiers are experimented using the Rattle tool. Rattle tool is a graphical user interface (GUI) in R package used to carry out data mining modeling using classifiers namely, tree, boost, random forest, support vector machine, logit and neural net. This study was conducted using simulation and real data in which the behaviors of the classifiers are observed based on accuracy, ROC curve and modeling time. Based on the simulation data, there is grouping of the algorithms in terms of accuracy. The first are logit, neural net and support vector machine. The second are boost and random forest and the third is decision tree. Based on the real data, the highest accuracy based on the training data is boost algorithm and based on the testing data the highest accuracy is the neural net algorithm. Overall, the support vector machine and neural net classifier are the two best classifiers in both simulation and real data.

Original languageEnglish
Title of host publicationSoft Computing in Data Science - 4th International Conference, SCDS 2018, Proceedings
EditorsBee Wah Yap, Azlinah Hj Mohamed, Michael W. Berry
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9789811334405
Publication statusPublished - 2019
Event4th International Conference on Soft Computing in Data Science, SCDS 2018 - Bangkok, Thailand
Duration: 15 Aug 201816 Aug 2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference4th International Conference on Soft Computing in Data Science, SCDS 2018


  • Accuracy
  • Classifier
  • Empirical data
  • Machine learning


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