Feature construction for reduction of tabular knowledge-based systems
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 construction methodology. The proposed method can be used synergistically with modularization to alleviate problems associated with combinatorial explosion in tabular knowledge bases.
feature construction tabular knowledge-based system machine learning