Choquet积分
可解释性
模糊测度理论
参数化复杂度
数学
度量(数据仓库)
模糊逻辑
回归
人工智能
计算机科学
回归分析
有界函数
机器学习
模糊集
模糊数
数据挖掘
统计
算法
数学分析
作者
Timothy C. Havens,Derek T. Anderson
出处
期刊:IEEE International Conference on Fuzzy Systems
日期:2019-06-01
被引量:12
标识
DOI:10.1109/fuzz-ieee.2019.8858835
摘要
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.
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