• Utpal Pal
• Sharmistha Bhattacharya (Halder)

The classical Decision Tree algorithms have been around for decades and Classification and Regression Tree (CART) is a most powerful technique among these, especially for classification and predictive analysis. But, the accuracy of CART degrades while dealing with high dimensional large data sets. Bayesian Decision Theoretic Rough Set (BDTRS) is a sophisticated rough set based method for attribute reduction. This paper represents the impact of BDTRS-based attribute reduction on CART. This paper also studies CART based on Pawlak rough set model. Experimental result, on R environment, using three different data sets shows that the BDTRS-based CART constructs improved decision tree for better classification efficiency.

CART, BDTRS, Pawlak rough set, Attribute Reduction, R

"Impact of Bayesian Decision Theoretic Rough Set-based Attribute Reduction on CART", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.336-342, July-2018, Available :http://www.jetir.org/papers/JETIR1807946.pdf

"Impact of Bayesian Decision Theoretic Rough Set-based Attribute Reduction on CART", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp336-342, July-2018, Available at : http://www.jetir.org/papers/JETIR1807946.pdf