Heterogeneous Ensemble Models For Generic Classification

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dc.contributor.authorBalogun, Abdullateef Oluwagbemiga
dc.contributor.authorBalogun, Adedayo Miftaudeen
dc.contributor.authorSadiku, Peter Ogirima
dc.contributor.authorAdeyemo, Victor Elijah
dc.date.accessioned2018-05-23T10:35:25Z
dc.date.available2018-05-23T10:35:25Z
dc.date.issued2017-05-10
dc.description.abstractThis 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.en_US
dc.identifier.citationBalogun, 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.en_US
dc.identifier.issn2065-7471
dc.identifier.urihttp://anale-informatica.tibiscus.ro/download/lucrari/15-1-11-Balogun.pdf
dc.identifier.urihttp://hdl.handle.net/123456789/262
dc.language.isoenen_US
dc.publisherComputers and Applied Computer Science Faculty in "Tibiscus" University of Timişoara, Romania.en_US
dc.relation.ispartofseriesVolume: 15;Issue: 1
dc.subjectMachine Learningen_US
dc.subjectData Miningen_US
dc.subjectFeature Selectionen_US
dc.subjectKnowledge Discoveryen_US
dc.titleHeterogeneous Ensemble Models For Generic Classificationen_US
dc.typeArticleen_US

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