Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

• Autorzy: Biju V. G. , Prashanth CM

Identyfikatory

DOI

10.1515/jacsm-2017-0002

• Języki publikacji: EN

Abstrakty

EN

This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

Słowa kluczowe

EN

bagging; boosting; SVM; KNN; decision tree

• Wydawca: Społeczna Akademia Nauk w Łodzi
• Czasopismo: Journal of Applied Computer Science Methods
• Rocznik: 2017
• Tom: Vol. 9 No. 1
• Strony: 23--47
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Biju V. G.

• Department of Computer Science and Engineering Christ University Faculty of Engineering, India

autor

Prashanth CM

• Department of Computer Science and Engineering Sapthgiri College of Engineering, India

Bibliografia

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