Machine learning of Choquet integral regression with respect to a bounded capacity (or non-monotonic fuzzy measure)

Authors

Timothy C. Havens, Michigan Technological UniversityFollow
Derek T. Anderson, University of Missouri

Document Type

Conference Proceeding

Publication Date

10-10-2019

Department

Department of Electrical and Computer Engineering

Abstract

Regression is the process of learning the relationship between sets of variables, enabling predictions of continuous output variables. Many approaches have been proposed to learn parameterized models with respect to numerous error metrics. In this paper, we propose a regression model based on the Choquet integral with respect to a bounded capacity (of which fuzzy measures are a subset). Our model has a generalized bias that enables capability beyond previously proposed Choquet integral regression approaches. We also develop an approach for learning the parameters of the Choquet integral regression from training data. Simulated and real-world benchmark data are used to demonstrate the performance of our regression approach compared with several competing regression methods. Results show that our approach has superior performance and is computationally very fast. An additional benefit of our Choquet integral regression is that it enables interpretability of the learned model, which we will explore in later works.

Publisher's Statement

©2019 IEEE. Publisher’s version of record: https://doi.org/10.1109/FUZZ-IEEE.2019.8858835

Publication Title

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Recommended Citation

Havens, T. C., & Anderson, D. T. (2019). Machine learning of Choquet integral regression with respect to a bounded capacity (or non-monotonic fuzzy measure). 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). http://doi.org/10.1109/FUZZ-IEEE.2019.8858835
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/933