BOOTS TRAPPING SUPERVISED CLASIFIER PARADIGM

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dc.contributor.authorOloyede, Isiaka
dc.date.accessioned2020-06-22T10:03:25Z
dc.date.available2020-06-22T10:03:25Z
dc.date.issued2017
dc.descriptionIn an attempt to elicit information from the data, estimation, detection and classification approaches are the tools commonly used by the research. Classification is one of the technique used to elicit information from the data in the way in which objects of features are separated into classes particularly when the dependent variable is categorical either binary or multiclass, Ahmad et. alen_US
dc.description.abstractThe study investigates the classification of learning algorithms in a bootstrap paradigm, the study examined features classification with binary class attributes in a bootstrap paradigm. Support Vector Machine, k-Nearest Neighbour, Random Forest, rpart, Artificial Neural Network and Naïve Bayes learning algorithms were compared. Accuracy, Prediction error, Sensitivity and Specificity were used as assessment criteria of the classifier after tuning to have minimum cost. The study therefore sample the training set and classifying each of the training set, the summary of the prediction error was obtained based on the testing dataset, the study showed that artificial neural network outperformed other learning algorithms with respect to accuracy criterion whereas the celebrated support vector machine performed poorly amongst the learning algorithms considered, the study depicted that artificial neural network outperformed other learning algorithms with the least misclassification error. The study depicted that K nearest neighbour outperformed other learning algorithms with highest sensitivity while ANN outperformed other learning algorithms with highest specificity. This study affirmed that there would be need to use more than a learning algorithm when there are irrelevant features in the data sets.en_US
dc.description.sponsorshipselfen_US
dc.identifier.citationoloyede i.(2017) Anale. Seria Informatică. Vol. XV fasc. 2 – 2017 Annals. Computer Science Series. 15th Tome 2nd Fasc. – 2017en_US
dc.identifier.urihttp://hdl.handle.net/123456789/4222
dc.language.isoenen_US
dc.publisherAnale. Seria Informatică. . Computer Science Series.en_US
dc.relation.ispartofseries;40
dc.subjectSVMen_US
dc.subjectNaïve Bayesen_US
dc.subjectBootstrap Classificationen_US
dc.subjectLearning algorithms.en_US
dc.titleBOOTS TRAPPING SUPERVISED CLASIFIER PARADIGMen_US
dc.typeArticleen_US

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