Study of Meta, Naïve Bayes and Decision Tree based Classifiers
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 8
Abstract
Classification deals with the kind of data mining problem which are concerned with prediction. Its main task is to classify the data in order to make predictions about new data. In general Classification can be viewed as the action or process of classifying something. In Data mining one of the most common tasks is to build models for the prediction of the class of an object on the basis of its attributes. This paper has made a comparative study of various classification algorithms viz. Meta Classifiers, Naïve Bayes and Decision Tree Based Classifiers. An experiment has been set up using different kinds of classification algorithms to test their performance. Theoretical analysis and experimental results will show that ‘Classification via Regression’ method has correctly classify all the instances, minimum errors are produced by ‘Decision Tree based Classifiers’, whereas ‘Naïve Bayes’ has classified all the instances in minimum time span.
Authors and Affiliations
Abhinay Bishnoi, Deepak Sinwar
Keywords
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