The results presented here (including the assessment of a new tool - inhibitory trees) offer
valuable tools for researchers in the areas of data mining knowledge discovery and machine
learning especially those whose work involves decision tables with many-valued decisions. The
authors consider various examples of problems and corresponding decision tables with
many-valued decisions discuss the difference between decision and inhibitory trees and rules
and develop tools for their analysis and design. Applications include the study of totally
optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory
trees and rules the comparison of greedy heuristics for tree and rule construction as
single-criterion and bi-criteria optimization algorithms and the development of a restricted
multi-pruning approach used in classification and knowledge representation.
