Abstract
This paper proposes a rule discovery tool for classification by using whale optimization algorithm that simulates the foraging behavior of humpback whales. Rule extraction is based on the optimization of randomly selected attributes according to rule fitness value. Algorithm were implemented and tested the most known 13 datasets and the results were compared with other known data mining algorithms including Decision Tree, Naïve Bayes, J48, JRip, Artificial Bee Colony and Ant Colony Optimization. The obtained results showed that whale optimization algorithm proved an appropriate candidate for classification processes.
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