A new similarity measure of IFSs and its applications

Tran Due Quynh, N. X. Thao, N. Thuan, Neuven Van Dinh
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引用次数: 4

Abstract

Similarity measures between Intuitionistic fuzzy sets (IFSs) play an important role and they have many applications in machine learning and multi-criteria decision making. However, there are few existing similarity measures. In this paper, we propose a new similarity measure between Intuitionistic fuzzy sets (IFSs). We first present a new mathematical formula and then prove that it satisfies all the conditions of similarity measures. The usefulness of the new similarity measure is pointed out by considering a simple classification problem. The results show that the proposed measure can be used to predict the class of a new sample while some of other measures cannot do it. Next, we apply the new similarity measure for solving multi criteria decision making (MCDM) problems. The results are compared with the ones by using some other similarity measures. The experimentation reports that the new similarity measure may provide different ranking of alternatives but it provides the same optimal solution.
一种新的ifs相似性度量方法及其应用
直觉模糊集(ifs)之间的相似性度量在机器学习和多准则决策中有着重要的应用。然而,现有的相似性度量方法很少。本文提出了一种新的直觉模糊集之间的相似性度量方法。我们首先提出了一个新的数学公式,然后证明了它满足相似性度量的所有条件。通过考虑一个简单的分类问题,指出了新的相似度度量的有效性。结果表明,本文提出的方法可以用来预测新样本的类别,而其他一些方法则不能。接下来,我们将新的相似度度量应用于解决多准则决策问题。并与其他相似度指标进行了比较。实验结果表明,新的相似性度量方法可以提供不同的备选排序,但提供相同的最优解。
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