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Yayın Feature extraction from discrete attributes(IEEE, 2010) Yıldız, Olcay TanerIn many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we extract new features by combining k discrete attributes, where for each subset of size k of the attributes, we generate all orderings of values of those attributes exhaustively. We then apply the usual univariate decision tree classifier using these orderings as the new attributes. Our simulation results on 16 datasets from UCI repository [2] show that the novel decision tree classifier performs better than the proper in terms of error rate and tree complexity. The same idea can also be applied to other univariate rule learning algorithms such as C4.5Rules [7] and Ripper [3].Yayın Searching for the optimal ordering of classes in rule induction(IEEE, 2012-11-15) Ata, Sezin; Yıldız, Olcay TanerRule induction algorithms such as Ripper, solve a K > 2 class problem by converting it into a sequence of K - 1 two-class problems. As a usual heuristic, the classes are fed into the algorithm in the order of increasing prior probabilities. In this paper, we propose two algorithms to improve this heuristic. The first algorithm starts with the ordering the heuristic provides and searches for better orderings by swapping consecutive classes. The second algorithm transforms the ordering search problem into an optimization problem and uses the solution of the optimization problem to extract the optimal ordering. We compared our algorithms with the original Ripper on 8 datasets from UCI repository [2]. Simulation results show that our algorithms produce rulesets that are significantly better than those produced by Ripper proper.
