Understanding imbalanced data: XAI & interpretable ML framework

IF 4.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Pub Date : 2024-01-16 DOI:10.1007/s10994-023-06414-w

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Abstract

There is a gap between current methods that explain deep learning models that work on imbalanced image data and the needs of the imbalanced learning community. Existing methods that explain imbalanced data are geared toward binary classification, single layer machine learning models and low dimensional data. Current eXplainable Artificial Intelligence (XAI) techniques for vision data mainly focus on mapping predictions of specific instances to inputs, instead of examining global data properties and complexities of entire classes. Therefore, there is a need for a framework that is tailored to modern deep networks, that incorporates large, high dimensional, multi-class datasets, and uncovers data complexities commonly found in imbalanced data. We propose a set of techniques that can be used by both deep learning model users to identify, visualize and understand class prototypes, sub-concepts and outlier instances; and by imbalanced learning algorithm developers to detect features and class exemplars that are key to model performance. The components of our framework can be applied sequentially in their entirety or individually, making it fully flexible to the user’s specific needs (https://github.com/dd1github/XAI_for_Imbalanced_Learning).

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理解不平衡数据：XAI 和可解释的 ML 框架

摘要当前解释不平衡图像数据的深度学习模型的方法与不平衡学习社区的需求之间存在差距。解释不平衡数据的现有方法面向二元分类、单层机器学习模型和低维数据。目前针对视觉数据的可解释人工智能（XAI）技术主要侧重于将特定实例的预测映射到输入，而不是检查全局数据属性和整个类别的复杂性。因此，我们需要一个为现代深度网络量身定制的框架，它能整合大型、高维、多类数据集，并揭示不平衡数据中常见的数据复杂性。我们提出了一套技术，可供深度学习模型用户用来识别、可视化和理解类原型、子概念和离群实例，也可供不平衡学习算法开发人员用来检测对模型性能起关键作用的特征和类范例。我们框架的各个组成部分既可以整体依次应用，也可以单独应用，因此完全可以灵活地满足用户的特定需求 (https://github.com/dd1github/XAI_for_Imbalanced_Learning)。

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来源期刊

Machine Learning 工程技术-计算机：人工智能

CiteScore

11.00

自引率

2.70%

发文量

162

审稿时长

3 months

期刊介绍： Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.