Optimization of Decision Rules Relative to Length Based on Modified Dynamic Programming Approach

Abstract

This chapter is devoted to the modification of an extension of dynamic programming approach for optimization of decision rules relative to length. “Classical” dynamic programming approach allows one to obtain optimal rules, i.e., rules with the minimum length. This fact is important from the point of view of knowledge representation. The idea of the dynamic programming approach for optimization of decision rules is based on a partitioning of a decision table into subtables. The algorithm constructs a directed acyclic graph. Basing on the constructed graph, sets of rules with the minimum length, attached to each row of a decision table, can be described. Proposed modification is based on the idea that not the complete graph is constructed but its part. It allows one to obtain values of length of decision rules close to optimal ones, and the size of the graph is smaller than in case of “classical” dynamic programming approach. The chapter also contains results of experiments with decision tables from UCI Machine Learning Repository.