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Comparative Study of Decision Trees and Rough Sets Theory as Knowledge ExtractionTools for Design and Control of Industrial Processes
Abstract:General requirements for knowledge representation in the form of logic rules, applicable to design and control of industrial processes, are formulated. Characteristic behavior of decision trees (DTs) and rough sets theory (RST) in rules extraction from recorded data is discussed and illustrated with simple examples. The significance of the models- drawbacks was evaluated, using simulated and industrial data sets. It is concluded that performance of DTs may be considerably poorer in several important aspects, compared to RST, particularly when not only a characterization of a problem is required, but also detailed and precise rules are needed, according to actual, specific problems to be solved.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333220Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1391
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