Abstract The accuracy-transparency trade-off is one of the most notable challenges when applying machine learning tools in the medical domain. Nefclass is a popular neuro-fuzzy classifier in medical diagnosis systems. Nefclass performs increasingly poorly as the data skewness increases. This paper presents a combined approach to improve the classification accuracy and interpretability of the Nefclass classifier, when feature values of the training and testing datasets exhibit positive skewness. The proposed model consists of two steps. Firstly, a modified Nefclass classifier embedded with a choice of two alternative discretization methods, MME and CAIM is implemented. Secondly, we devised a new rule pruning method based on the Habermans' adjusted residual to reduce the size of the resulting ruleset. This rule-pruning method improves the interpretability of Nefclass without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches provides a considerable improvement in classification accuracy and transparency of Nefclass on skewed data.

中文翻译：

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一种改进的 NEFCLASS 分类器，对具有倾斜特征值的数据集具有增强的准确性-可解释性权衡

摘要 在医学领域应用机器学习工具时，准确性-透明度的权衡是最显着的挑战之一。Nefclass 是医学诊断系统中流行的神经模糊分类器。随着数据偏度的增加，Nefclass 的性能越来越差。当训练和测试数据集的特征值表现出正偏度时，本文提出了一种组合方法来提高 Nefclass 分类器的分类精度和可解释性。所提出的模型包括两个步骤。首先，实现了一个改进的 Nefclass 分类器，嵌入了两种可选的离散化方法，MME 和 CAIM。其次，我们设计了一种基于 Habermans 调整残差的新规则修剪方法，以减少生成的规则集的大小。这种规则修剪方法提高了 Nefclass 的可解释性，而不会显着降低准确性。此外，结合这两种方法的混合方法显着提高了 Nefclass 在倾斜数据上的分类准确性和透明度。