Comparative Analysis of Classification Methods in R Environment with two Different Data Sets

Authors(2) :-B Nithya, Dr. V Ilango

Machine Learning methods are widely used in various domains as they are influential in classification and prediction processes. The frequently used supervised machine learning task is classification. There are various types of classification algorithms with strengths and weaknesses appropriate for different types of input data. This paper depicts the implementation of few classification methods such as Decision Tree, K Nearest Neighbour and Nave Byes classifier for different datasets in R environment. This paper presents the comparative study of these methods using open source tool R. The aim of this paper is to analyse the performance of these methods in two different datasets based on the evaluation metrics like accuracy and error rate. The implementation procedure show that the performance of any classification algorithm is based on the type of attributes of datasets and their characteristics. This paper shows that based on the constraints, requirements with type of input datasets specific algorithm and tool can be chosen for implementation.

Authors and Affiliations

B Nithya
Senior Assistant Professor & Research Scholar, Department of MCA, New Horizon College of Engineering, Bangalore, India
Dr. V Ilango
Professor, Department of MCA, New Horizon College of Engineering, Bangalore, India

Machine Learning, Classification, Decision Tree, K Nearest Neighbour, Nave Bayes Classifier, Performance, R Tool.

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 136-141
Manuscript Number : CSEIT172612
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

B Nithya, Dr. V Ilango, "Comparative Analysis of Classification Methods in R Environment with two Different Data Sets", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.136-141, November-December.2017
URL : http://ijsrcseit.com/CSEIT172612

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