Restructuring decision tables for elucidation of knowledge

Rattikorn Hewett, John Leuchner

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Decision tables are widely used in many knowledge-based and decision support systems. They allow relatively complex logical relationships to be represented in an easily understood form and processed efficiently. This paper describes second-order decision tables (decision tables that contain rows whose components have sets of atomic values) and their role in knowledge engineering to: (1) support efficient management and enhance comprehensibility of tabular knowledge acquired by knowledge engineers, and (2) automatically generate knowledge from a tabular set of examples. We show how second-order decision tables can be used to restructure acquired tabular knowledge into a condensed but logically equivalent second-order table. We then present the results of experiments with such restructuring. Next, we describe SORCER, a learning system that induces second-order decision tables from a given database. We compare SORCER with IDTM, a system that induces standard decision tables, and a state-of-the-art decision tree learner, C4.5. Results show that in spite of its simple induction methods, on the average over the data sets studied, SORCER has the lowest error rate.

Original languageEnglish
Pages (from-to)271-290
Number of pages20
JournalData and Knowledge Engineering
Issue number3
StatePublished - Sep 2003


  • Knowledge acquisition
  • Knowledge engineering
  • Knowledge-based systems
  • Machine learning
  • Representation


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