Notes on “On (IO,O)-fuzzy rough sets based on overlap functions”

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2022-11-01 DOI:10.1016/j.ijar.2022.08.010

Chun Yong Wang , Sheng Nan Xu , Lijuan Wan

引用次数: 0

Abstract

Qiao investigated the properties and topological structures of $(I_{O}, O)$ -fuzzy rough sets, which extended the classical conjunction operator in rough approximation operator to an overlap function O. However, there are some faults in the characterizations of $(I_{O}, O)$ -fuzzy rough sets, such as wrong conclusions and strict condition, even if the overlap function O is assumed to be a continuous t-norm with no non-trivial zero divisors. This paper further discusses $(I_{O}, O)$ -fuzzy rough sets and rectifies those faults. Moreover, some examples are presented to show those faults.

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关于“基于重叠函数的on (IO,O)-模糊粗糙集”的注解

Qiao研究了(IO,O)-模糊粗糙集的性质和拓扑结构，将粗糙逼近算子中的经典合算符推广到一个重叠函数O。然而，在(IO,O)-模糊粗糙集的刻画中，即使假设重叠函数O是一个没有非平凡零因子的连续t模，也存在一些错误的结论和严格的条件。本文进一步讨论了(IO,O)-模糊粗糙集，并对这些错误进行了修正。并举例说明了这些错误。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.