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Influence of T-norm and T-conorm operators in Fuzzy ID3 algorithm

机译:T范数和T范数运算符在模糊ID3算法中的影响

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Fuzzy ID3 algorithm is a widely studied classification algorithm in order to induce fuzzy decision trees. The structure of fuzzy decision trees combines the interpretability of the decision trees with the capability of fuzzy logic on uncertainty. The fuzzy inference procedure of fuzzy decision trees is a crucial manner for the classification problems. So, the choice of the aggregation operators for the inference procedure is very important. The aim of this study is to view the performance of Fuzzy inference procedure of Fuzzy ID3 algorithm within different triangular norm (T-norm) and triangular conorm (T-conorm) operators. The fuzzy inference procedure was applied by using four different T-norm and T-conorm operators (Zadeh, Product-Sum_Umano, Yager and Hamacher). These operators were applied within the fuzzy inference phase of the system. Fuzzy c-means clustering algorithm (FCM) was used for the fuzzification of datasets. After the pre-fuzzification process, Fuzzy ID3 algorithm was performed on seven numerical datasets (Appendicitis, Balance, Iris, Wine, Hearth, New Thyroid, Haberman) selected from the KEEL data set repository. The study was encouraged with the statistical tests. The hypothesis validation techniques were used. In future studies, it is expected that by using different parameters for parametrized operators, the best accuracy rates can be handled.
机译:Fuzzy ID3算法是一种广泛研究的分类算法,用于引入模糊决策树。模糊决策树的结构将决策树的可解释性与模糊逻辑对不确定性的能力结合在一起。模糊决策树的模糊推理过程是分类问题的关键方法。因此,为推理过程选择聚合运算符非常重要。这项研究的目的是查看在不同的三角范数(T-norm)和三角conorm(T-conorm)算子中Fuzzy ID3算法的Fuzzy推理过程的性能。通过使用四个不同的T-范数和T-conorm运算符(Zadeh,Product-Sum_Umano,Yager和Hamacher)来应用模糊推理过程。这些算子被应用在系统的模糊推理阶段。模糊c均值聚类算法(FCM)用于数据集的模糊化。在预模糊化过程之后,对从KEEL数据集存储库中选择的七个数值数据集(阑尾炎,天平,虹膜,酒,炉膛,新甲状腺,哈伯曼)执行Fuzzy ID3算法。统计学测试鼓励了这项研究。使用假设验证技术。在未来的研究中,期望通过对参数化运算符使用不同的参数,可以处理最佳准确率。

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