Under-sampling method based on cooperative co-evolutionary mechanism
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摘要: 針對非平衡數據集分類中"少數類樣本精度難以提高"這一瓶頸問題,提出了一種基于協同進化機制的欠采樣方法.此方法將少數類樣本與多數類樣本劃分為兩類種群,采用種群協同進化原理,利用提出的動態交叉變異算子自適應協同進化過程,實現種群間自動調節和自動適應.仿真試驗結果表明,此采樣方法增強了局部隨機搜索能力,改善了種群的分布特性,加強了算法的全局收斂能力,在不降低多數類樣本分類性能的基礎上有效提高了少數類樣本的精度.與其他經典重采樣方法相比,本文辦法抗噪能力好,具有更強的魯棒性.Abstract: For the bottleneck of improving the accuracy of minority class samples within the paradigm of imbalanced datasets,a novel under-sampling method based on the cooperative co-evolutionary mechanism was presented in this paper.During the employment of the method,the majority and the minority samples were divided into two populations,which adopted the cooperative co-evolutionary mechanism,dynamically adaptive crossovers and mutation operators to automatically adjust the evolution process within populations.Simulation results prove that the method enhances the capacity of local search,improves the distribution characteristics of populations and strengthens the capacity of global convergence.Moreover,the method notably improves the accuracy of the minority samples without degrading that of the majority ones.Compared to other classical resampling methods,the method shows good noise immunity with more powerful robustness.
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Key words:
- imbalanced datasets /
- classification /
- sampling /
- cooperative co-evolution /
- adaptive algorithms
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