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Maximizing minority accuracy for imbalanced pattern classification problems using cost-sensitive Localized Generalization Error Model
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-02-16 , DOI: 10.1016/j.asoc.2021.107178
Wing W.Y. Ng , Zhengxi Liu , Jianjun Zhang , Witold Pedrycz

Traditional machine learning methods may not yield satisfactory generalization capability when samples in different classes are imbalanced. These methods tend to sacrifice the accuracy of the minority class to improve the overall accuracy without regarding the fact that misclassifications of minority samples usually costs more in many real world applications. Therefore, we propose a neural network training method via a minimization of the cost-sensitive localized generalization error-based objective function (c-LGEM) to achieve a better balance of error yielded by the minority and the majority classes. The c-LGEM emphasizes the minimization of the generalization error of the minority class in a cost-sensitive manner. Experimental results obtained on 16 UCI datasets show that neural networks trained by the c-LGEM yield better performance in comparison to the performance yielded by some existing methods.



中文翻译:

使用成本敏感的局部广义误差模型最大化不平衡模式分类问题的少数派准确性

当不同类别的样本不平衡时,传统的机器学习方法可能无法产生令人满意的泛化能力。这些方法往往会牺牲少数类的准确性以提高整体准确性,而没有考虑到在许多实际应用中少数类样本的错误分类通常会花费更多的事实。因此,我们提出了一种通过最小化成本敏感的基于局部广义误差的目标函数(c-LGEM)的神经网络训练方法,以实现少数派和多数派产生的错误的更好平衡。c-LGEM强调以成本敏感的方式最小化少数群体的泛化误差。

更新日期:2021-02-22
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