基于模糊信息的颗粒球离群值检测器

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qilin Li , Zhong Yuan , Dezhong Peng , Xiaomin Song , Huiming Zheng , Xinyu Su
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引用次数: 0

摘要

离群点检测是进行数据挖掘和分析过程中的重要组成部分,已被应用于许多领域。现有的方法通常固定在单样本处理范例中,其中处理单元是每个单独的单粒度样本。这种处理范例效率低下,并且忽略了数据中固有的多粒度特性。此外,这些方法往往忽略了数据中存在的不确定性信息。为了弥补上述缺点,我们提出了一种基于颗粒球模糊颗粒(GBFG)的无监督离群值检测方法。GBFG采用基于颗粒球的计算范式,其中基本处理单元是颗粒球。这种从单个样本到颗粒球的转变使GBFG能够从多粒度的角度捕获整体数据结构,并提高离群值检测的性能。随后,我们根据样本所属的颗粒球模糊颗粒的离群度计算离群因子,作为样本离群度的度量。实验结果表明,与现有的优秀算法相比,GBFG具有显著的性能。GBFG的代码可在https://github.com/Mxeron/GBFG上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Granular-ball fuzzy information-based outlier detector
Outlier detection is an important part of the process of carrying out data mining and analysis and has been applied to many fields. Existing methods are typically anchored in a single-sample processing paradigm, where the processing unit is each individual and single-granularity sample. This processing paradigm is inefficient and ignores the multi-granularity features inherent in data. In addition, these methods often overlook the uncertainty information present in the data. To remedy the above-mentioned shortcomings, we propose an unsupervised outlier detection method based on Granular-Ball Fuzzy Granules (GBFG). GBFG adopts a granular-ball-based computing paradigm, where the fundamental processing units are granular-balls. This shift from individual samples to granular-balls enables GBFG to capture the overall data structure from a multi-granularity perspective and improve the performance of outlier detection. Subsequently, we calculate the outlier factor based on the outlier degrees of the granular-ball fuzzy granules to which the sample belongs, serving as a measure of the outlier degrees of samples. The experimental results prove that GBFG has a remarkable performance compared with the existing excellent algorithms. The code of GBFG is publicly available on https://github.com/Mxeron/GBFG.
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来源期刊
International Journal of Approximate Reasoning
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.
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