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A nonparametric ensemble binary classifier and its statistical properties

Author

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  • Chakraborty, Tanujit
  • Chakraborty, Ashis Kumar
  • Murthy, C.A.

Abstract

In this work, we propose an ensemble of classification trees (CT) and artificial neural networks (ANN). Several statistical properties including universal consistency and upper bound of an important parameter of the proposed classifier are shown. Numerical evidence is also provided using various real-life data sets to assess the performance of the model. Our proposed nonparametric ensemble classifier does not suffer from the “curse of dimensionality” and can be used in a wide variety of feature selection cum classification problems. Performance of the proposed model is quite better when compared to many other state-of-the-art models used for similar situations.

Suggested Citation

  • Chakraborty, Tanujit & Chakraborty, Ashis Kumar & Murthy, C.A., 2019. "A nonparametric ensemble binary classifier and its statistical properties," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 16-23.
    • Handle: RePEc:eee:stapro:v:149:y:2019:i:c:p:16-23
    DOI: 10.1016/j.spl.2019.01.021
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    References listed on IDEAS

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    1. Tanujit Chakraborty & Swarup Chattopadhyay & Ashis Kumar Chakraborty, 2018. "A novel hybridization of classification trees and artificial neural networks for selection of students in a business school," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 434-446, June.
    2. Dunson, David B., 2018. "Statistics in the big data era: Failures of the machine," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 4-9.
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    Cited by:

    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. Tanujit Chakraborty & Ashis Kumar Chakraborty & Zubia Mansoor, 2019. "A hybrid regression model for water quality prediction," OPSEARCH, Springer;Operational Research Society of India, vol. 56(4), pages 1167-1178, December.

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