Explainable and Local Correction of Classification Models Using Decision Trees

Authors

  • Hirofumi Suzuki Fujitsu Limited
  • Hiroaki Iwashita Fujitsu Limited
  • Takuya Takagi Fujitsu Limited
  • Keisuke Goto Fujitsu Limited
  • Yuta Fujishige Fujitsu Limited
  • Satoshi Hara Osaka University

DOI:

https://doi.org/10.1609/aaai.v36i8.20816

Keywords:

Machine Learning (ML)

Abstract

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.

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Published

2022-06-28

How to Cite

Suzuki, H., Iwashita, H., Takagi, T., Goto, K., Fujishige, Y., & Hara, S. (2022). Explainable and Local Correction of Classification Models Using Decision Trees. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 8404-8413. https://doi.org/10.1609/aaai.v36i8.20816

Issue

Section

AAAI Technical Track on Machine Learning III