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Recently, it has been shown that, in ensemble learning, it may be preferable to ensemble some instead of all the classifiers. Various selective ensemble approaches are then designed, where optimization algorithms like genetic algorithm (GA) are used to evolve weights of component classifiers and classifiers with weights greater than a threshold are selected. This paper proposes a novel selective ensemble...
Classification rule mining is an important data mining process that aims to discover a small set of rules from the training data set with predetermined targets. The shuffled frog leaping(SFL) algorithm, is a new robust evolutionary algorithm based on the local search and the shuffling processes. In this paper, an efficient SFL-based classification rule mining algorithm is proposed. The experimental...
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