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Generating Counterfactual Instances for Explainable Class-Imbalance Learning
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DOI：10.1109/TKDE.2023.3302847.png)
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摘要
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Existing class imbalance learning paradigms focus on lifting the importance of minority instance, aiming to improve the model in terms of certain evaluation metrics (e.g., AUC and $F_{1}$F1-measure). One drawback of these methods is that they lack enough transparency, hence, cannot be fully trusted in vital domains. To this end, this paper deal with the class imbalance learning task with counterfactual instances. Given an instance and a classifier, a counterfactual is a fake instance which, while having smallest distance to the original instance, is classified as a different class by the classifier. Therefore, the most important features for a classifier can be identified by inspecting the difference between an instance and its counterfactual. To utilize counterfactuals, a novel Explainable Generative Adversarial Network (EXGAN) is proposed. EXGAN has a unique two generators versus multiple discriminators architecture where the generators are used to generate effective counterfactuals and discriminators are trained for the class imbalance learning task. In addition to the architecture, an innovative ensemble loss function ensuring each discriminator complementing each other is designed to overcome the class imbalance issue. Extensive experiments prove that the counterfactuals generated by EXGAN can be used to produce effective local explanation and provide significant better class imbalance learning ability than existing competitors.
Keyword:
Generators
Classification algorithms
Task analysis
Standards
Ensemble learning
Costs
Computational modeling
Class imbalance learning
counterfactual
explainable machine learning
explainable generative adversarial network
ensemble learning
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10.4
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6.9K
被引数:
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