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

Authors

  • 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

Keywords:

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

Abstract

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.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-10-31

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Volume 2, Issue 5, September-October-2017

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Research Articles

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How to Cite

[1]
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.