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A clustering-based discretization for supervised learning

Author

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  • Gupta, Ankit
  • Mehrotra, Kishan G.
  • Mohan, Chilukuri

Abstract

We address the problem of discretization of continuous variables for machine learning classification algorithms. Existing procedures do not use interdependence between the variables towards this goal. Our proposed method uses clustering to exploit such interdependence. Numerical results show that this improves the classification performance in almost all cases. Even if an existing algorithm can successfully operate with continuous variables, better performance is obtained if the variables are first discretized. An additional advantage of discretization is that it reduces the overall computation time.

Suggested Citation

  • Gupta, Ankit & Mehrotra, Kishan G. & Mohan, Chilukuri, 2010. "A clustering-based discretization for supervised learning," Statistics & Probability Letters, Elsevier, vol. 80(9-10), pages 816-824, May.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:9-10:p:816-824
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    References listed on IDEAS

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    1. Mehrotra, Kishan G. & Ozgencil, Necati E. & McCracken, Nancy, 2007. "Squeezing the last drop: Cluster-based classification algorithm," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1288-1299, July.
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    Cited by:

    1. Zhao, Pengxiang & Dong, Zhao Yang & Meng, Ke & Kong, Weicong & Yang, Jiajia, 2021. "Household power usage pattern filtering-based residential electricity plan recommender system," Applied Energy, Elsevier, vol. 298(C).

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