3 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 3 / 3
Yayın Solving the multiple level warehouse layout problem using ant colony optimization(Springer Verlag, 2020-03-01) Arnaout, Jean Paul M.; ElKhoury, Caline; Karayaz, GamzeThis paper addresses the multiple level warehouse layout problem, which involves assigning items to cells and levels with the objective of minimizing transportation costs. A monthly demand and an inventory requirement are associated with every item type along with vertical and horizontal unit transportation costs. The warehouse has one port to transport items vertically from ground floor to the other levels, where each item must be assigned to exactly one cell on the assigned level. An ant colony optimization (ACO) algorithm is adapted to this NP-complete problem and its performance is evaluated by comparing its solutions to the ones obtained using genetic algorithms (GA) as well as the optimal solutions for small problems. The computational results reflected the superiority of ACO in large-size problem instances, with a marginally better performance than GA in smaller ones, while solving the tested instances within a reasonable computational time. Furthermore, ACO was able to attain most of the known optimal solutions for small-size problem instances.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.Yayın Omnivariate rule induction using a novel pairwise statistical test(IEEE Computer Soc, 2013-09) Yıldız, Olcay TanerRule learning algorithms, for example, RIPPER, induces univariate rules, that is, a propositional condition in a rule uses only one feature. In this paper, we propose an omnivariate induction of rules where under each condition, both a univariate and a multivariate condition are trained, and the best is chosen according to a novel statistical test. This paper has three main contributions: First, we propose a novel statistical test, the combined 5 x 2 cv t test, to compare two classifiers, which is a variant of the 5 x 2 cv t test and give the connections to other tests as 5 x 2 cv F test and k-fold paired t test. Second, we propose a multivariate version of RIPPER, where support vector machine with linear kernel is used to find multivariate linear conditions. Third, we propose an omnivariate version of RIPPER, where the model selection is done via the combined 5 x 2 cv t test. Our results indicate that 1) the combined 5 x 2 cv t test has higher power (lower type II error), lower type I error, and higher replicability compared to the 5 x 2 cv t test, 2) omnivariate rules are better in that they choose whichever condition is more accurate, selecting the right model automatically and separately for each condition in a rule.
