Abstract
The Iterative Dichotomiser 3 (ID3) recursively partitions the problem domain, producing the subsequent partitions as decision trees. However, the classification accuracy of ID3 decreases in dealing with very large datasets. Maximum probabilistic-based rough set (MPBRS) is a sophisticated approach for insignificant feature reduction. This paper represents an application of MPBRS on ID3 as a method for insignificant attribute elimination. The paper further investigates ID3 using Pawlak rough set and also Bayesian decision-theoretic rough set (BDTRS) for comparison. The experimental result, using R language, on datasets collected from UCI Machine-Learning repository shows that MPBRS-based ID3 induces enriched decision tree resulting in improved classification.
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