PEARL: Prototype learning via rule learning

Tianfan Fu, Tian Gao, Cao Xiao, Tengfei Ma, Jimeng Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep neural networks have demonstrated promising prediction performance on many health analytics tasks. However, the interpretability of the deep models is often lacking. In comparison, classical interpretable models such as decision rule learning do not lead to the same level of accuracy as deep neural networks (DNN) and can also be too complex to interpret (e.g., due to large tree depths). In this work, we propose Prototype LeArNing via Rule Learning (PEARL), which iteratively constructs a decision rule list to guide a neural network to learn representative prototypes that can be explained by the associated rules. The resulting prototype neural network inherits both the prediction power of DNNs and interpretability associated with rules, thus can provide accurate and interpretable predictions. Evaluated on real world health datasets, PEARL demonstrates state-of-the-art accuracy to various DNN baselines and interpretable results that are simpler than standard decision trees can provide.

Original languageEnglish (US)
Title of host publicationACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
EditorsXinghua Shi, Michael Buck, Jian Ma, Pierangelo Veltri
PublisherAssociation for Computing Machinery, Inc
Pages223-232
Number of pages10
ISBN (Electronic)9781450366663
DOIs
StatePublished - Sep 4 2019
Externally publishedYes
Event10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2019 - Niagara Falls, United States
Duration: Sep 7 2019Sep 10 2019

Publication series

NameACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Conference

Conference10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2019
CountryUnited States
CityNiagara Falls
Period9/7/199/10/19

Keywords

  • Deep Learning
  • Healthcare
  • Interpretable Machine Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Biomedical Engineering
  • Health Informatics

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