An attribute ranking method based on rough sets and interval-valued fuzzy sets

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

International Journal of Approximate Reasoning Pub Date : 2024-05-21 DOI:10.1016/j.ijar.2024.109215

Bich Khue Vo , Hung Son Nguyen

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引用次数: 0

Abstract

Feature importance is a complex issue in machine learning, as determining a superior attribute is vague, uncertain, and dependent on the model. This study introduces a rough-fuzzy hybrid (RAFAR) method that merges various techniques from rough set theory and fuzzy set theory to tackle uncertainty in attribute importance and ranking. RAFAR utilizes an interval-valued fuzzy matrix to depict preference between attribute pairs. This research focuses on constructing these matrices from datasets and identifying suitable rankings based on these matrices. The concept of interval-valued weight vectors is introduced to represent attribute importance, and their additive and multiplicative compatibility is examined. The properties of these consistency types and the efficient algorithms for solving related problems are discussed. These new theoretical findings are valuable for creating effective optimization models and algorithms within the RAFAR framework. Additionally, novel approaches for constructing pairwise comparison matrices and enhancing the scalability of RAFAR are suggested. The study also includes experimental results on benchmark datasets to demonstrate the accuracy of the proposed solutions.

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基于粗糙集和区间值模糊集的属性排序法

特征重要性是机器学习中的一个复杂问题，因为确定一个优越的属性是模糊的、不确定的，并且取决于模型。本研究介绍了一种粗糙模糊混合（RAFAR）方法，该方法融合了粗糙集理论和模糊集理论的各种技术，以解决属性重要性和排序中的不确定性问题。RAFAR 利用区间值模糊矩阵来描述属性对之间的偏好。这项研究的重点是从数据集中构建这些矩阵，并根据这些矩阵确定合适的排序。引入了区间值权重向量的概念来表示属性的重要性，并研究了它们的加法和乘法兼容性。讨论了这些一致性类型的属性以及解决相关问题的高效算法。这些新的理论发现对于在 RAFAR 框架内创建有效的优化模型和算法非常有价值。此外，还提出了构建成对比较矩阵和增强 RAFAR 可扩展性的新方法。研究还包括基准数据集的实验结果，以证明所提解决方案的准确性。

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.