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Interpretation and Simplification of Deep Forest

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posted on 20.01.2020 by Sangwon Kim, Mira Jeong, Byoung Chul Ko

This paper proposes a new method for interpreting and simplifying a black box model of a deep random forest (RF) using a proposed rule elimination. In deep RF, a large number of decision trees are connected to multiple layers, thereby making an analysis difficult. It has a high performance similar to that of a deep neural network (DNN), but achieves a better generalizability. Therefore, in this study, we consider quantifying the feature contributions and frequency of the fully trained deep RF in the form of a decision rule set. The feature contributions provide a basis for determining how features affect the decision process in a rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Consequently, the simplified model has fewer parameters and rules than before. Experiment results have shown that a feature contribution analysis allows a black box model to be decomposed for quantitatively interpreting a rule set. The proposed method was successfully applied to various deep RF models and benchmark datasets while maintaining a robust performance despite the elimination of a large number of rules.

History

Email Address of Submitting Author

eddiesangwonkim@gmail.com

ORCID of Submitting Author

0000-0002-7452-3897

Submitting Author's Institution

Keimyung University

Submitting Author's Country

Korea

Licence

Exports