Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms
A common information representation task in research as well as educational and statistical practice is to comprehensively and intuitively express data in two-dimensional tables. Examples include tables in scientific papers, as well as reports and the popular press. Data is often simple enough for users to reorder. In many other cases though, there are complex data patterns that make finding the best re-arrangement of rows and columns for optimum readability a tough problem. We propose that row and column ordering should be regarded as a combinatorial optimization problem and solved using evolutionary computation techniques. The use of genetic algorithms has already been proposed in the literature. This paper proposes for the first time the use of estimation of distribution algorithms for table ordering. We also propose alternative ways of representing the problem in order to reduce its dimensionality. By learning a selective naive Bayes classifier, we can find out how to jointly combine the parameters of these algorithms to get good table orderings. Experimental examples in this paper are on 2D tables.
estimation of distribution algorithms selective naive bayes bertin matrices ordering of tables conciseness parametrization