摘要翻译：
基于模糊规则的分类系统是用于模式分类问题的最流行的模糊建模系统之一。本文研究了在基于模糊规则的分类系统中应用9种不同的T-范数的效果。近年来，在基于模糊规则的分类系统的构建中，数据挖掘领域中的可信度和支持度的模糊版本被广泛应用于规则的选择和加权。为了计算这些优点，乘积通常被用作t范数。本文采用不同的t范数来计算置信度和支持度。因此，在构造基于模糊规则的分类系统的过程中，对规则选择和规则加权步骤的计算进行了改进。因此，计算中的这些变化导致了基于规则的分类系统的总体精度的改变。在一些知名数据集上的实验结果表明，在分类精度方面，采用Aczel-Alsina算子的性能最好，其次是Dubois-Prade，第三是Dombi算子。在实验中，我们使用了来自加州大学欧文分校机器学习库(UCI)的12个具有数值属性的数据集。
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英文标题：
《Comparison of different T-norm operators in classification problems》
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作者：
Fahimeh Farahbod and Mahdi Eftekhari
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Fuzzy rule based classification systems are one of the most popular fuzzy modeling systems used in pattern classification problems. This paper investigates the effect of applying nine different T-norms in fuzzy rule based classification systems. In the recent researches, fuzzy versions of confidence and support merits from the field of data mining have been widely used for both rules selecting and weighting in the construction of fuzzy rule based classification systems. For calculating these merits the product has been usually used as a T-norm. In this paper different T-norms have been used for calculating the confidence and support measures. Therefore, the calculations in rule selection and rule weighting steps (in the process of constructing the fuzzy rule based classification systems) are modified by employing these T-norms. Consequently, these changes in calculation results in altering the overall accuracy of rule based classification systems. Experimental results obtained on some well-known data sets show that the best performance is produced by employing the Aczel-Alsina operator in terms of the classification accuracy, the second best operator is Dubois-Prade and the third best operator is Dombi. In experiments, we have used 12 data sets with numerical attributes from the University of California, Irvine machine learning repository (UCI).
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PDF链接：
https://arxiv.org/pdf/1208.1955