Softmax函数
计算机科学
人工智能
模糊逻辑
数据挖掘
分类器(UML)
机器学习
模式识别(心理学)
熵(时间箭头)
人工神经网络
物理
热力学
作者
Xiaoyu Han,Xiubin Zhu,Witold Pedrycz,Almetwally M. Mostafa,Zhiwu Li
标识
DOI:10.1016/j.asoc.2024.111498
摘要
Takagi–Sugeno–Kang (TSK) classifiers have achieved great success in many applications due to their interpretability and transparent model reliability for users. At present, however, how to evaluate classification results is still an unsolved issue for TSK classifiers. This study designs a fuzzy rule-based classifier based on TSK classifiers, the outputs of which for an instance can be considered as the membership grades that the instance belongs to all classes. Then, an information entropy-based method is proposed to estimate the certainty of the outputs, which facilitates the further evaluation of the classification results of the instance for users. If the confidence level is not high, users can reject the classification results, and use other more advanced classifiers or collect more information about the instance. Moreover, the developed mechanism is suitable for handling large data since the adaptive moment estimation algorithm is used to identify the parameters of it. Experimental results demonstrate that the developed mechanism outperforms several rule-based classifiers.
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