Bilal Alatas

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Artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. Like the chaos in real bee colony behavior, this paper proposes new ABC algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions. This has been(More)
In this paper, a Pareto-based multi-objective differential evolution (DE) algorithm is proposed as a search strategy for mining accurate and comprehensible numeric association rules (ARs) which are optimal in the wider sense that no other rules are superior to them when all objectives are simultaneously considered. The proposed DE guided the search of ARs(More)
In this paper, a genetic algorithm (GA) is proposed as a search strategy for not only positive but also negative quantitative association rule (AR) mining within databases. Contrary to the methods used as usual, ARs are directly mined without generating frequent itemsets. The proposed GA performs a database-independent approach that does not rely upon the(More)
Classification is a supervised learning method that induces a classification model from a database and is one of the most commonly applied data mining task. The frequently employed techniques are decision tree or neural network-based classification algorithms. This work presents an efficient genetic algorithm (GA) for classification rule mining technique(More)