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Material Classification from Imprecise Chemical Composition : Probabilistic vs Possibilistic Approach

机译:从不精确的化学成分进行材料分类:概率与可能性方法

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In this paper we propose a method of explainable material classification from imprecise chemical compositions. The problem of classification from imprecise data is addressed with a fuzzy decision tree whose terms are learned by a clustering algorithm. We deduce fuzzy rules from the tree, which will provide a justification of the result of the classification. Two opposed approaches are compared : the probabilistic approach and the possibilistic approach.
机译:在本文中,我们提出了一种根据不精确化学成分对材料进行分类的方法。通过模糊决策树解决了从不精确数据分类的问题,该决策树的术语是通过聚类算法学习的。我们从树上推导出模糊规则,这将为分类结果提供依据。比较了两种相反的方法:概率方法和可能性方法。

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