Development of a Rule Based Classification System to Identify a Suitable Classifier for a Particular Dataset.

Authors(3) :-Subham Datta, Gautam, Tapas Saha

Big data in the present world have a tremendous scope of research along with machine learning. Given a large amount of data, performing operations on these data is a very tedious task. One such process involves the classification of these huge amounts of the dataset. For classification of a dataset, we use different set of classifiers. The dataset, when tested on these classifiers, shows some results which are obviously different from each other. For a known dataset and the set of classifiers, we know the classifier from the set of classifiers that gives the best result. However, it might be the case that some unknown, a newly created or a modified dataset, the set of classifiers which gives the best result is a real challenge [14]. In this paper, we have applied 16 classifiers on IRIS dataset and the experimental results show GRID search classifier provides the best accuracy. So, from here on, we can conclude that the dataset which has similarity with IRIS dataset, GRID search classifier can be applied to get high accuracy as compared to other classifiers.

Authors and Affiliations

Subham Datta
Department of Computer Science, Pondicherry University, Puducherry, India
Gautam
Department of Computer Science, Pondicherry University, Puducherry, India
Tapas Saha
Department of Computer Science, Pondicherry University, Puducherry, India

Classification, Holdout Method, Rule Based Classification, GRID Search.

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 478-487
Manuscript Number : CSEIT1725106
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Subham Datta, Gautam, Tapas Saha, "Development of a Rule Based Classification System to Identify a Suitable Classifier for a Particular Dataset.", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.478-487, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT1725106

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