Binary hierarchical multiclass classifier for uncertain numerical features

机译：不确定数值特征的二元分层多类分类器

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Real-world multiclass classification problems involve moderately high dimensional inputs with a large number of class labels. As well, for most real-world applications, uncertainty has to be handled carefully, unless the classification results could be inaccurate or even incorrect. In this paper, we investigate a binary hierarchical partitioning of the output space in an uncertain framework to overcome these limitations and yield better solutions. Uncertainty is modeled within the quantitative possibility theory framework. Experimentations on real ultrasonic dataset show good performances of the proposed multiclass classifier. An accuracy rate of 93% has been achieved.

机译：现实世界中的多类分类问题涉及具有大量类标签的中等高维输入。同样，对于大多数实际应用，不确定性必须小心处理，除非分类结果可能不准确甚至不正确。在本文中，我们研究在不确定的框架中对输出空间进行二进制分层划分，以克服这些限制并产生更好的解决方案。不确定性是在定量可能性理论框架内建模的。在真实的超声波数据集上的实验显示了所提出的多分类器的良好性能。达到了93％的准确率。

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《International Conference on Advanced Technologies for Signal and Image Processing》|2020年|1-6|共6页
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Marwa Chakroun; Amal Charfi; Sonda Ammar Bouhamed; Imene Khanfir Kallel; Basel Solaiman; Houda Derbel;
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Binary hierarchical classification; Uncertainty; Possibility theory; Ultrasonic signal processing;

机译：二元分类法;不确定度;可能性理论;超声信号处理;

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Binary hierarchical multiclass classifier for uncertain numerical features

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