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A Study on CART Based on Maximum Probabilistic-Based Rough Set

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Mining Intelligence and Knowledge Exploration (MIKE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

The Classification and Regression Tree (CART) recursively partitions the measurement space, displaying the resulting partitions as decision tree. However, the performance of CART-based decision tree degrades while dealing with high-dimensional large data sets. This research work studies CART, based on Maximum Probabilistic-based Rough Set (MPBRS). The MPBRS has been used as a tool for insignificant data reduction without sacrificing information content. This paper also studies CART, based on Pawlak rough set and Bayesian Decision Theoretic Rough Set (BDTRS) for comparative analysis. Experimental results on three different data sets show that the MPBRS-based CART constructs improved decision tree for better classification efficiency.

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Authors and Affiliations

  1. Tripura University, Tripura, India

    Utpal Pal, Sharmistha Bhattacharya (Halder) & Kalyani Debnath

Authors
  1. Utpal Pal
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  2. Sharmistha Bhattacharya (Halder)
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  3. Kalyani Debnath
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Corresponding author

Correspondence to Utpal Pal .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Pal, U., Bhattacharya (Halder), S., Debnath, K. (2017). A Study on CART Based on Maximum Probabilistic-Based Rough Set. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_39

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  • DOI: https://doi.org/10.1007/978-3-319-71928-3_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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