The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan’s C4.5 are also made.
CITATION STYLE
Clark, P., & Boswell, R. (1991). Rule induction with CN2: Some recent improvements. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 482 LNAI, pp. 151–163). Springer Verlag. https://doi.org/10.1007/BFb0017011
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