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Inspired by an upward stochastic walking idea, a new ensemble pruning method called simulated quenching walking (SQWALKING) is developed in this paper. The rationale behind this method is to give values to stochastic movements as well as to accept unvalued solutions during the investigation of search spaces. SQWALKING incorporates simulated quenching and forward selection methods to choose the models...
Discovering the effective subset of models in a pool of classifiers is an important and remarkable topic in ensemble learning scope. Using meticulously selected subset instead of entire ensemble leads to more efficient and effective results. This paper introduces a novel hybrid ensemble selection method of firefly and forward search algorithms. Because of the two different selection phases in the...
Hill climbing algorithm is one of the famous optimization algorithms which has been applied to solve the problem of pruning an ensemble of classifiers. In this study, we propose an ensemble pruning method using Hill Climbing algorithm whose evaluation measure is “Human-Like Foresight” (HLF). To invent this novel measure, we are inspired by human foresight in facing different situations in his life...
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