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A meta-heuristic approach for improving the accuracy in some classification algorithms
2011
Computers & Operations Research
Current classification algorithms usually do not try to achieve a balance between fitting and generalization when they infer models from training data. Furthermore, current algorithms ignore the fact that there may be different penalty costs for the false-positive, false-negative, and unclassifiable types. Thus, their performance may not be optimal or may even be coincidental. This paper proposes a meta-heuristic approach, called the Convexity Based Algorithm (CBA), to address these issues. The
doi:10.1016/j.cor.2010.04.011
fatcat:eow4yrcfqnggplj4y4up5ph4ge