Heterogeneous Ensemble Models For Generic Classification

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Computers and Applied Computer Science Faculty in "Tibiscus" University of Timişoara, Romania.


This paper presents the application of somedata mining techniques in the field of health care and computer network security. The selected classifiers wer eused individually and also, they were ensemble methods using four different combinations for the purpose of classification. Naïve Bayes, Radial Basis Function and Ripper algorithms were selected and the ensemble methods were majority voting, multi-scheme, stacking and Minimum Probability. The KDDCup’99 dataset was used as the benchmark for computer network security, while for the health care, breast cancer and diabetes dataset from the WEKA repository were used. All experiments and simulations were carried out, analyzed and evaluated using the WEKA tool. The Multi-scheme ensemble method gave the best accuracy result for the KDD dataset (99.81%) and the breast cancer dataset (73.08%) but its value of (75.65%) on breast cancer is the least of them all. Ripper algorithm gave the best result accuracy (99.76%) on KDD dataset amongst the base classifier but it was slightly behind in the breast cancer and diabetes dataset.



Machine Learning, Data Mining, Feature Selection, Knowledge Discovery


Balogun, A. O., Balogun, A. M., Sadiku, P. O., & Adeyemo, V. E. (2017): Heterogeneous Ensemble Models For Generic Classification. Annals Computer Science Series 15th Tome 1st , Fascicle- 2017 Paper 15-1-11. 92-98.