A Research Travelogue on Classification Algorithms using R Programming
S.Nagaparameshwara Chary1, B Rama2
1S.Nagaparameshwara chary, Research Scholar in Department of Computer Science, Kakatiya University, Warangal,
2B Rama, Assistant Professor in Department of Computer Science, Kakatiya University, Warangal.

Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9155-9158 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9014118419/2019©BEIESP | DOI: 10.35940/ijrte.D9014.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Classification is a machine learning task which consists in predicting the set association of unclassified examples, whose label is not known, by the properties of examples in a representation learned earlier as of training examples, that label was known. Classification tasks contain a huge assortment of domains and real world purpose: disciplines such as medical diagnosis, bioinformatics, financial engineering and image recognition between others, where domain experts can use the model erudite to sustain their decisions. All the Classification Approaches proposed in this paper were evaluate in an appropriate experimental framework in R Programming Language and the major emphasis is on k-nearest neighbor method which supports vector machines and decision trees over large number of data sets with varied dimensionality and by comparing their performance against other state-of-the-art methods. In this process the experimental results obtained have been verified by statistical tests which support the better performance of the methods. In this paper we have survey various classification techniques of Data Mining and then compared them by using diverse datasets from “University of California: Irvine (UCI) Machine Learning Repository” for acquiring the accurate calculations on Iris Data set.
Keywords: Classification, Data Mining, Decision Table, kNN, SVM.
Scope of the Article: Classification.