Abstract
In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our interpretable algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is exponentially large given long and complicated text data, we design a learnable anchor algorithm to better extract local and domain knowledge-oriented explanations. A set of regulations is further introduced, combining learned interpretable representations with anchors and information extraction to generate comprehensible semantic explanations. To carry out an extensive experiment, we first develop a drug abuse ontology (DAO) on a drug abuse dataset on the Twittersphere, and a consumer complaint ontology (ConsO) on a consumer complaint dataset, especially for interpretable ML. Our experimental results show that our approach generates more precise and more insightful explanations compared with a variety of baseline approaches.
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Data is available at https://www.consumerfinance.gov/data-research/consumer-complaints/2
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Acknowledgements
The authors gratefully acknowledge the support from the National Science Foundation (NSF) Grant Nos. CNS-1747798, CNS-1850094, and NSF Center for Big Learning (Oregon). We thank for the financial support from Wells Fargo. We also thank Nisansa de Silva (University of Oregon, USA) for valuable discussions and Anuja Badeti (New Jersey Institute of Technology, USA) for helping with data processing. Pelin Ayranci and Phung Lai are co-first authors.
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PA Design and implement the algorithms, and write the paper. PL Design and implement the algorithms, and write the paper. NP Ensured that the descriptions are accurate and agreed by all authors, Provide very detailed supervision of this work. DN Provide very detailed supervision of this work. A K provide very detailed supervision of this work. DD Provide very detailed supervision of this work. HH Data Curation, Processing, and Analysis.
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Code is available at https://github.com/PhungLai728/OnML
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An earlier version of this paper was presented at the IJCNN Conference.
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Ayranci, P., Lai, P., Phan, N. et al. OnML: an ontology-based approach for interpretable machine learning. J Comb Optim 44, 770–793 (2022). https://doi.org/10.1007/s10878-022-00856-z
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DOI: https://doi.org/10.1007/s10878-022-00856-z