Experimental evaluation of three partition

Decision table decomposition is a machine learning approach that decomposes a given decision table into an equivalent hierarchy of decision tables. The approach aims to discover decision tables that are overall less complex than the initial one, potentially easier to interpret, and introduce new and meaningful intermediate concepts. Since an exhaustive search for an optimal hierarchy of decision tables is prohibitively complex, the decomposition uses a suboptimal iterative algorithm that requires the so-called partition selection criterion to decide among possible candidates for decomposition. This paper introduces two such criteria and experimentally compares their performance with the criteria originally used for the decomposition of Boolean functions. Two of these criteria are additionally used to assess the overall complexity of discovered decision tables. The experiments highlight the diierences between the criteria, but also show that in all three cases the decomposition may discover meaningful intermediate concepts and relatively compact decision tables.