Information Technology and Management

, Volume 6, Issue 4, pp 351–362 | Cite as

Feature Selection for Reduction of Tabular Knowledge-Based Systems

  • Selwyn Piramuthu


Tabular knowledge-based systems are known to be extremely versatile for verification and validation of knowledge bases. However, a major disadvantage of these systems is the combinatorial explosion that accompanies addition of new attributes or condition entries in the table. One of the means of alleviating this problem in tabular knowledge-based systems is through modularization, which is the process of breaking a big comprehensive table into smaller tables that are easy to deal with. In this study, we propose and illustrate another means to deal with this problem through use of feature selection methodology. The proposed method can be used synergistically with modularization to alleviate problems associated with combinatorial explosion in tabular knowledge bases.


feature selection tabular knowledge-based system machine learning 


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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Decision and Information SciencesUniversity of FloridaGainesville

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