论文标题
通过基于优势的粗糙集方法预测开创性的质量
Predicting Seminal Quality with the Dominance-Based Rough Sets Approach
论文作者
论文摘要
该论文依靠先前发表的研究的临床数据。我们确定了对上述工作的两个非常可疑的假设，即混淆了缺乏和缺乏证据的证据，并忽略了属性领域的顺序性质。然后，我们表明，使用适当的序数方法（例如基于优势的粗糙集方法（DRSA））可以显着提高专家系统的预测准确性，从而使100个实例的数据集几乎完全准确。除了DRSA在解决手头诊断问题方面的表现外，这些结果还表明，基础数据集的不足和琐碎性。我们提供了来自UCI机器学习存储库的打开数据的链接，以便轻松地验证/反驳本文中提出的索赔。
The paper relies on the clinical data of a previously published study. We identify two very questionable assumptions of said work, namely confusing evidence of absence and absence of evidence, and neglecting the ordinal nature of attributes' domains. We then show that using an adequate ordinal methodology such as the dominance-based rough sets approach (DRSA) can significantly improve the predictive accuracy of the expert system, resulting in almost complete accuracy for a dataset of 100 instances. Beyond the performance of DRSA in solving the diagnosis problem at hand, these results suggest the inadequacy and triviality of the underlying dataset. We provide links to open data from the UCI machine learning repository to allow for an easy verification/refutation of the claims made in this paper.
