Incremental construction of rule ensembles using classifiers produced by different class orderings
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CitationYildiz, O. T., & Ulas, A. (2016). Incremental construction of rule ensembles using classifiers produced by different class orderings. Paper presented at the 492-497. doi:10.1109/ICPR.2016.7899682
In this paper, we discuss a novel approach to incrementally construct a rule ensemble. The approach constructs an ensemble from a dynamically generated set of rule classifiers. Each classifier in this set is trained by using a different class ordering. We investigate criteria including accuracy, ensemble size, and the role of starting point in the search. Fusion is done by averaging. Using 22 data sets, floating search finds small, accurate ensembles in polynomial time.