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dc.contributor.authorGrzymala-Busse, Dobroslawa M.
dc.contributor.authorGrzymala-Busse, Jerzy W.
dc.date.accessioned2005-05-18T11:45:29Z
dc.date.available2005-05-18T11:45:29Z
dc.date.issued1995-05
dc.identifier.citationGRZYMALABUSSE, DM; GRZYMALABUSSE, JW. The usefulness of a machine learning approach to knowledge acquisition . COMPUTATIONAL INTELLIGENCE. May 1995. 11(2):268-279.
dc.identifier.otherISI:A1995TD20000005
dc.identifier.urihttp://hdl.handle.net/1808/416
dc.description.abstractThis paper presents results of experiments showing how machine learning methods are useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to test how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.
dc.format.extent42309 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherBLACKWELL PUBLISHERS
dc.subjectRule-based system
dc.subjectLearning from examples
dc.subjectIncomplete knowledge
dc.subjectKnowledge acquisition
dc.titleThe usefulness of a machine learning approach to knowledge acquisition
dc.typeArticle
dc.identifier.doi10.1111/j.1467-8640.1995.tb00032.x
dc.rights.accessrightsopenAccess


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