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Comparison of Different Classification Techniques Using Different Datasets

  • Nitesh Kumar
  • Souvik Mitra
  • Madhurima Bhattacharjee
  • Lopa Mandal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 811)

Abstract

Big data analytics is considered to be the future of information technology in today's world, which incorporates data mining to be one of its most promising tool. The present work illustrates a comparative study to find out which kind of classifiers work better with which kind of datasets. It illustrates comparisons of the efficiency of the different classifiers focusing on numeric and text data. Datasets from IMDb and 20newsgroups have been used for the purpose. Current work mainly focuses on comparing different algorithms such as Decision Stump, Decision Table, K-Star, REPTree and ZeroR in the area of numeric classification, and evaluation of the efficiency of Naive Bayes classifier for text classification. The result in this paper suggests the best and worst of the test parameters, as it widens the scope of their usage on the basis of types and the size of datasets.

Keywords

Data mining Text classification Numeric data classification Classifier algorithms 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Tata Consultancy Services LimitedBengaluruIndia
  2. 2.Institute of Engineering & ManagementKolkataIndia

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