Abstract:
Several discretization algorithms have been developed, but efficient, accurate discretization remains a challenge. This paper proposes SPID5, a new discretization algorithm based on two supervised heuristics, one local and one global, that synergize.
The local heuristic is the well-known information gain of continuous attributes, and the global heuristic is a novel concept of iterative data set noise reduction. By decreasing pseudo deletion count, noise is reduced.
Using six cutting-edge classifiers and 35 real-world data sets from the standard UCI data repository, SPID5 is compared to three well-known discretization algorithms. SPID5 outperforms all three discretization algorithms in classification accuracy and noise reduction.
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