Journal
INFORMATION SCIENCES
Volume 593, Issue -, Pages 155-176Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.02.003
Keywords
Feature importance; Feature interaction; Classification; Regression; Explainable artificial intelligence
Categories
Funding
- Institute of Information & communications Technology Planning & Evaluation (IITP) - Korea government (MSIT) [2018-0-00242, 2021-0-01531]
Ask authors/readers for more resources
This paper proposes a new method for measuring feature importance and interaction. For the classification model, cases with correct predictions are grouped based on their characteristics, while for the regression model, cases are grouped based on the change in prediction error. The proposed method supports understanding of feature importance and interaction, and decomposes feature importance into feature power and feature interactions.
Feature importance and interaction are among the main issues in explainable artificial intelligence or interpretable machine learning. To measure feature importance and interaction, several methods, such as H-statistic and partial dependency, have been proposed. However, it is difficult to understand the practical implications of importance and interaction. In this paper, a new method for measuring feature importance and interaction is proposed. For the classification model, we observed correctly predicted cases in a predictive model and grouped them according to the characteristics of the cases. We derived a method for feature importance and interaction from group information. For the regression model, we grouped cases according to the change in the size of the prediction error. The proposed method supports the same rationale for feature importance and interaction. It also supports the decomposition of feature importance to feature power and feature interactions. To implement the proposed method, three visualization tools, including a feature interaction graph, are implemented. Through the proposed work, we can better understand the working mechanism of a predictive model.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available