This paper presents an efficient approach to the Supervised MDS method. This method handles the problems of data visualization, supervised classification and bipartite ranking. In order to overcome the non-differentiable nature of the Supervised MDS method, the mathematical formulation proposed in this work is based on the hyperbolic smoothing technique. The performance of the algorithm is evaluated by computational experiments. The results show that the proposed methodology presented, in most cases, better results than the results available in the literature. Furthermore, the methodology presents a good performance in relation to the methods Logistic regression, Naive Bayes and Support Vector Machine.

中文翻译：

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一种应用于监督 MDS 方法的平滑优化方法

本文提出了一种有效的监督 MDS 方法。该方法处理数据可视化、监督分类和二分排序问题。为了克服监督 MDS 方法的不可微性，本工作中提出的数学公式基于双曲线平滑技术。通过计算实验评估算法的性能。结果表明，在大多数情况下，所提出的方法呈现出比文献中可用的结果更好的结果。此外，该方法相对于逻辑回归、朴素贝叶斯和支持向量机方法具有良好的性能。