基于优势度的粗糙集方法在精液质量预测中的应用
Predicting Seminal Quality with the Dominance-Based Rough Sets Approach
摘要：
该论文依赖于之前发表的一项研究的临床数据。我们确定了两个非常有问题的假设说，工作，即混淆证据的缺乏和缺乏的证据，并忽略了属性域的顺序性质。然后，我们表明，使用适当的顺序方法，如基于优势的粗糙集方法（DRSA）可以显着提高预测精度的专家系统，导致几乎完全准确的数据集的100个实例。除了DRSA在解决手头的诊断问题方面的表现之外，这些结果表明了基础数据集的不足和琐碎。我们提供了来自UCI机器学习存储库的开放数据的链接，以便轻松验证/反驳本文中的主张。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
