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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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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