Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA
Автор: Yugal kumar, G. Sahoo
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 7 Vol. 4, 2012 года.
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In the field of Machine learning & Data Mining, lot of work had been done to construct new classification techniques/ classifiers and lot of research is going on to construct further new classifiers with the help of nature inspired technique such as Genetic Algorithm, Ant Colony Optimization, Bee Colony Optimization, Neural Network, Particle Swarm Optimization etc. Many researchers provided comparative study/ analysis of classification techniques. But this paper deals with another form of analysis of classification techniques i.e. parametric and non parametric classifiers analysis. This paper identifies parametric & non parametric classifiers that are used in classification process and provides tree representation of these classifiers. For the analysis purpose, four classifiers are used in which two of them are parametric and rest of are non-parametric in nature.
Bayesian Net, Logistic Regression, Multi layer perceptron, and Navie Bayes
Короткий адрес: https://sciup.org/15011715
IDR: 15011715
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