论文标题
基于优势的粗糙设定方法，基本思想和主要趋势
Dominance-based Rough Set Approach, basic ideas and main trends
论文作者
论文摘要
基于主导性的粗略方法（DRSA）已被提议作为机器学习和知识发现方法来处理多个标准决策（MCDA）。由于其能力要求决策者（DM）提供简单的偏好信息并提供易于理解和可解释的建议，因此在这些年份中，Drsa引起了很多兴趣，现在它是最受欢迎的MCDA方法之一。实际上，它也已被应用于MCDA域之外，作为一种经常知识发现和数据挖掘方法，用于分析单调（以及非单调）数据。在这一贡献中，我们回想起DRSA的基本原理和主要概念，并概述了其发展和软件。我们还介绍了该方法的起源的历史重建，特别关注罗马·斯文斯基的贡献。
Dominance-based Rough Approach (DRSA) has been proposed as a machine learning and knowledge discovery methodology to handle Multiple Criteria Decision Aiding (MCDA). Due to its capacity of asking the decision maker (DM) for simple preference information and supplying easily understandable and explainable recommendations, DRSA gained much interest during the years and it is now one of the most appreciated MCDA approaches. In fact, it has been applied also beyond MCDA domain, as a general knowledge discovery and data mining methodology for the analysis of monotonic (and also non-monotonic) data. In this contribution, we recall the basic principles and the main concepts of DRSA, with a general overview of its developments and software. We present also a historical reconstruction of the genesis of the methodology, with a specific focus on the contribution of Roman Słowiński.
