Inducing oblique decision trees with evolutionary algorithms

Evolution of Decision Trees

This paper addresses the issue of the induction of orthogonal, oblique and multivariate decision trees. Algorithms proposed by other researchers use heuristic, usually based on the information gain concept, to induce decision trees greedily. These algorithms are often tailored for a given tree type (e.g orthogonal), not being able to induce other types of decision trees. Our work presents an al...

Combining Evolutionary Algorithms With Oblique Decision Trees to Detect Bent-Double Galaxies

Pareto-optimality of oblique decision trees from evolutionary algorithms

This paper investigates the performance of evolutionary algorithms in the optimization aspects of oblique decision tree construction and describes their performance with respect to classification accuracy, tree size, and Pareto-optimality of their solution sets. The performance of the evolutionary algorithms is analyzed and compared to the performance of exhaustive (traditional) decision tree c...

Using Evolutionary Algorithms to Induce Oblique Decision Trees

This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that this can be accomplished in a shorter time. Experiments were performed with a (1+1) evolutionary strategy and a simple geneti...

Rough Set Approach to Multivariate Decision Trees Inducing

Aimed at the problem of huge computation, large tree size and over-fitting of the testing data for multivariate decision tree (MDT) algorithms, we proposed a novel roughset-based multivariate decision trees (RSMDT) method. In this paper, the positive region degree of condition attributes with respect to decision attributes in rough set theory is used for selecting attributes in multivariate tes...