Explainable and Local Correction of Classification Models Using Decision Trees
Explainable and Local Correction of Classification Models Using Decision Trees
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DOI：
10.1609/aaai.v36i8.20816
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Hirofumi Suzuki;Hiroaki Iwashita;Takuya Takagi;Keisuke Goto;Yuta Fujishige;Satoshi Hara
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文献类型：
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作者：
Hirofumi Suzuki;Hiroaki Iwashita;Takuya Takagi;Keisuke Goto;Yuta Fujishige;Satoshi Hara
在实际的机器学习中，模型经常被更新或修正，以适应新的数据集。在本研究中，我们对模型校正提出了两个挑战。首先，需要明确地描述更正对最终用户的影响，类似于将更正描述为发布说明的标准软件。其次，修正量必须小，以便修正后的模型与旧模型的表现相似。在本研究中，我们提出了解决这两个挑战的分类模型的第一个模型校正方法。我们的想法是使用一个额外的决策树来纠正旧模型的输出。由于决策树的可解释性，更正对最终用户是可描述的，这解决了第一个挑战。我们通过在训练额外的决策树时加入修正的数量来解决第二个挑战，从而使修正的影响很小。实际数据的实验验证了该方法与现有校正方法的有效性。
In practical machine learning, models are frequently updated, or corrected, to adapt to new datasets. In this study, we pose two challenges to model correction. First, the effects of corrections to the end-users need to be described explicitly, similar to standard software where the corrections are described as release notes. Second, the amount of corrections need to be small so that the corrected models perform similarly to the old models. In this study, we propose the first model correction method for classification models that resolves these two challenges. Our idea is to use an additional decision tree to correct the output of the old models. Thanks to the explainability of decision trees, the corrections are describable to the end-users, which resolves the first challenge. We resolve the second challenge by incorporating the amount of corrections when training the additional decision tree so that the effects of corrections to be small. Experiments on real data confirm the effectiveness of the proposed method compared to existing correction methods.