Pipelining machine learning algorithms for knowledge discovery

Allan L. Egbert Jr.; Robert Chris Lacher

doi:10.1117/12.380565

30 March 2000 Pipelining machine learning algorithms for knowledge discovery

Allan L. Egbert Jr., Robert Chris Lacher

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380565
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

A rule-generating algorithm, Incremental Reduced Error Pruning (IREP), has been proposed by Furnkranz and Widmer. A modified IREP algorithm (RIPPERk) may be applied to raw data representing a classification problem. Introduced by Cohen, 1995, RIPPERk generates a set of hypotheses in the form of if-then rules. The resulting solution maybe coarse or compete, covering all outlyers in the classification data set.

Citation Download Citation

Allan L. Egbert Jr. and Robert Chris Lacher "Pipelining machine learning algorithms for knowledge discovery", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380565

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