BOOTS TRAPPING SUPERVISED CLASIFIER PARADIGM
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Date
2017
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Anale. Seria Informatică. . Computer Science Series.
Abstract
The 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.
Description
In 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. al
Keywords
SVM, Naïve Bayes, Bootstrap Classification, Learning algorithms.
Citation
oloyede i.(2017) Anale. Seria Informatică. Vol. XV fasc. 2 – 2017 Annals. Computer Science Series. 15th Tome 2nd Fasc. – 2017