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A case study on machine learning and classification

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  • Amit Kumar
  • Bikash Kanti Sarkar

Abstract

As a young research field, the machine learning has made significant progress and covered a broad spectrum of applications for the last few decades. Classification is an important task of machine learning. Today, the task is used in a vast array of areas. The present article provides a case study on various classification algorithms (under machine learning), their applicability and issues. More specifically, a step by step progress on this area is discussed in this paper. Further, an experiment is conducted over 12 real-world datasets drawn from University of California, Irvine (UCI, a machine learning repository) using four competent individual learners namely, C4.5 (decision tree-based classifier), Naïve Bayes, k-nearest neighbours (k-NN), neural network and two hybrid learners: Bagging (based on decision tree) and (fuzzy + rough-set + k-NN: a hybrid system) for head to head comparison of their classification performance. Their merits and demerits (as discussed in this article) are analysed accordingly with the obtained results.

Suggested Citation

  • Amit Kumar & Bikash Kanti Sarkar, 2017. "A case study on machine learning and classification," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 9(2), pages 179-208.
    • Handle: RePEc:ids:ijidsc:v:9:y:2017:i:2:p:179-208
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    Cited by:

    1. Parimal Kumar Giri & Sagar S. De & Sachidananda Dehuri & Sung‐Bae Cho, 2021. "Biogeography based optimization for mining rules to assess credit risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 35-51, January.

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