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granular computing framework for credit card fraud detection

ALEXANDRIA ENGINEERING JOURNAL 2025年 121卷 387-401页

作者： Ayoub, Mniai Abdelhamid, Tamouh Khalid, Jebari Abdelmalek Essaadi Univ IABML FSTT Tanger 90000 Morocco

Online credit card fraud detection presents significant challenges due to the dynamic and sophisticated nature of fraudulent activities. Fraudulent transactions are rare compared to legitimate ones, leading to highly imbalanced datasets that hinder traditional machine learning models from effectively identifying anomalies. Additionally, fraud patterns evolve rapidly as cybercriminals adopt new techniques, requiring detection systems to be adaptive and robust. The presence of irrelevant or noisy attributes in transactional data further complicates the process, potentially masking fraudulent activities and degrading model performance. Furthermore, striking a balance between minimizing false positives and detecting true frauds is a critical and ongoing challenge in this domain. This research proposes the use of a granular computing framework (GrCF) to enhance the performance of credit card fraud detection. This research highlights the strengths of the model by implementing a strategy based on three essential pillars. The model uses case-based reasoning (CBR) in conjunction with a mixed sampling technique to address the missing variables. The initial stage focuses on the distribution of classes within the dataset. The second step, the fuzzy rough set, refines the feature selection (FS) process by concentrating on the most important properties. In the third step, we develop the machine learning model by using the Boosted GWO (BGWO) method to maximize the hyperparameters of the Support Vector Data Description (SVDD). We have demonstrated through several tests that the proposed framework surpasses some current algorithms inaccuracy and efficiency.

关键词： granular computing Fuzzy rough set Metaheuristic Features selection

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granular computing for Machine Learning: Pursuing New Development Horizons

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第1期55卷 460-471页

作者： Pedrycz, Witold Silesian Tech Univ Fac Automat Control Elect & Comp Sci Div Measurements & Control Syst PL-44100 Gliwice Poland Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Istinye Univ Fac Engn & Nat Sci Dept Comp Engn Sariyer Istanbul 34010 Turkiye

Undoubtedly, machine learning (ML) has demonstrated a wealth of far-reaching successes present both at the level of fundamental developments, design methodologies and numerous application areas, quite often encountered in domains requiring a high level of autonomous behavior. Over the passage of time, there are growing challenges of privacy and security, interpretability, explainability, confidence (credibility), and computational sustainability, among others. In this study, we advocate that these quests could be addressed by casting them both conceptually and algorithmically in the unified environment augmented by the principles of granular computing. It is demonstrated that the level of abstraction, delivered by granular computing plays a pivotal role in the interpretation by quantifying the level of credibility of ML constructs. The study also highlights the principles of granular computing and elaborates on its landscape. The original idea of a comprehensive and unified framework of data-knowledge environment of ML is introduced along with a detailed discussion on how data and knowledge are used in a seamless fashion by invoking granular embedding and producing relevant loss functions. Key categories of knowledge-data integration realized at the levels of data and model (involving symbolic/qualitative models and physics-oriented models) and investigated.

关键词： granular computing Fuzzy sets Computational modeling Data models Rough sets Probabilistic logic Machine learning Optimization Numerical models Indexes Gaussian process (GP) granular computing granular embedding information granules knowledge-data design environment machine learning (ML)

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Incremental learning and granular computing from evolving data streams: An application to speech-based bipolar disorder diagnosis

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FUZZY SETS AND SYSTEMS 2025年 500卷

作者： Leite, Daniel Casalino, Gabriella Kaczmarek-Majer, Katarzyna Castellano, Giovanna Paderborn Univ Dept Comp Sci Paderborn Germany Univ Bari Aldo Moro Dept Comp Sci Bari Italy Polish Acad Sci Syst Res Inst Warsaw Poland Univ Ostrava Inst Res & Applicat Fuzzy Modeling Ostrava Czech Republic

We apply an evolving granular-computing modeling approach, called evolving Optimal granular System (eOGS), to bipolar mood disorder (BD) diagnosis based on speech data streams. The eOGS online learning algorithm reveals information granules in the flow and design the structure and parameters of a granular rule-based model with a certain degree of interpretability based on acoustic attributes obtained from phone calls made over 7 months to the Psychiatry department of a hospital. A multi-objective programming problem that trades-off information specificity, model compactness, and numerical and granular error indices is presented. Spectral and prosodic attributes are ranked and selected based on a hybrid Pearson-Spearman correlation coefficient. Low attribute-class correlation, ranging from 0.03 to 0.07, is observed, as well as high class overlap, which is typical in the psychiatric field. eOGS models for BD recognition overcome alternative computational-intelligence models, namely, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Fuzzy-set-Based evolving Modeling (FBeM-Gauss), by a small margin in both best and average cases;followed by eXtended Takagi-Sugeno (xTS) and evolving Takagi Sugeno (eTS) types of models. The proposed eOGS model using only 8 of the original acoustic attributes, and about 15 'If-Then' inference rules, has exhibited the best root mean square error, 0.1361, and 91.8% accuracy in sharp BD class estimates. Granules associated to linguistic labels and a granular input-output map offer human understandability with relation to the inherent process of generating class estimates. Linguistically readable eOGS rules may assist physicians in explaining symptoms and making a diagnosis.

关键词： Signal processing granular computing Online machine learning Explainable artificial intelligence AI in medicine

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Integrating granular computing with density estimation for anomaly detection in high-dimensional heterogeneous data

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INFORMATION SCIENCES 2025年 690卷

作者： Chen, Baiyang Yuan, Zhong Peng, Dezhong Chen, Xiaoliang Chen, Hongmei Chen, Yingke Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Natl Innovat New Vis UHD Video Technol Co Chengdu 610095 Peoples R China Univ Montreal Dept Comp Sci & Operat Res Montreal PQ H3C 3J7 Canada Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Detecting anomalies in complex data is crucial for knowledge discovery and data mining across a wide range of applications. While density-based methods are effective for handling varying data densities and diverse distributions, they often struggle with accurately estimating densities in heterogeneous, uncertain data and capturing interdependencies among features in high-dimensional spaces. This paper proposes a fuzzy granule density-based anomaly detection algorithm (GDAD) for heterogeneous data. Specifically, GDAD first partitions high-dimensional attributes into subspaces based on their interdependencies and employs fuzzy information granules to represent data. The core of the method is the definition of fuzzy granule density, which leverages local neighborhood information alongside global density patterns and effectively characterizes anomalies in data. Each object is then assigned a fuzzy granule density-based anomaly factor, reflecting its likelihood of being anomalous. Through extensive experimentation on various real-world datasets, GDAD has demonstrated superior performance, matching or surpassing existing state-of-the-art methods. GDAD's integration of granular computing with density estimation provides a practical framework for anomaly detection in high-dimensional heterogeneous data.

关键词： Anomaly detection granular computing Fuzzy information granule Fuzzy granule density High-dimensional heterogeneous data

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GBCT: Efficient and Adaptive Clustering via granular-Ball computing for Complex Data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Xia, Shuyin Shi, Bolun Wang, Yifan Xie, Jiang Wang, Guoyin Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Key Lab Cyberspace Big Data Intelligent Secur Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Dept Comp Sci Chongqing 400065 Peoples R China

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in the human brain, resulting in those methodsbad performance in efficiency, generalization ability, and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering via granular-ball computing. First, clustering algorithm based on granular-ball (GBCT) generates a smaller number of granular-balls to represent the original data and forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. At the same time, its coarse-grained characteristics are not susceptible to noise, and the algorithm is efficient and robust;besides, as granular-balls can fit various complex data, GBCT performs much better in nonspherical datasets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also be used to improve other traditional methods.

关键词： Clustering algorithms Partitioning algorithms Computational modeling Robustness Noise Approximation algorithms Mathematical models Clustering methods Telecommunications Shape Clustering granular computing granular-ball multigranularity

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granular computing for Data Analytics:A Manifesto of Human-Centric computing

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1025-1034页

作者： Witold Pedrycz Department of Electrical&Computer Engineering University of Alberta EdmontonCanadaand also with the Systems Research InstitutePolish Academy of SciencesWarsawPoland

In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the *** advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through granular *** computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing *** study identifies the principles of granular computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.

关键词： Aggregation clustering design of information granules fuzzy sets granular computing information granules,principle of justifiable granularity.

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granular computing and Knowledge Reduction in Formal Contexts

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2009年第10期21卷 1461-1474页

作者： Wu, Wei-Zhi Leung, Yee Mi, Ju-Sheng Zhejiang Ocean Univ Sch Math Phys & Informat Sci Zhoushan 316004 Zhejiang Peoples R China Chinese Univ Hong Kong Dept Geog & Resource Management Ctr Environm Policy & Resource Management Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Hong Kong Hong Kong Peoples R China Hebei Normal Univ Coll Math & Informat Sci Shijiazhuang 050016 Hebei Peoples R China

granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper. Information granules and their properties in a formal context are first discussed. Concepts of a granular consistent set and a granular reduct in the formal context are then introduced. Discernibility matrices and Boolean functions are, respectively, employed to determine granular consistent sets and calculate granular reducts in formal contexts. Methods of knowledge reduction in a consistent formal decision context are also explored. Finally, knowledge hidden in such a context is unraveled in the form of compact implication rules.

关键词： Concept lattices data mining formal contexts granular computing granules knowledge reduction rough sets

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granular computing: Perspectives and Challenges

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IEEE TRANSACTIONS ON CYBERNETICS 2013年第6期43卷 1977-1989页

作者： Yao, JingTao Vasilakos, Athanasios V. Pedrycz, Witold Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada Kuwait Univ Dept Comp Sci Kuwait Kuwait Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2G7 Canada Polish Acad Sci Syst Res Inst PL-01224 Warsaw Poland

granular computing, as a new and rapidly growing paradigm of information processing, has attracted many researchers and practitioners. granular computing is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research. We first review some basic notions of granular computing. Classification and descriptions of various schools of research in granular computing are given. We also present and identify some research directions in granular computing.

关键词： granular computing granular relationships granules information processing

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granular computing neural-fuzzy modelling: A neutrosophic approach

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APPLIED SOFT computing 2013年第9期13卷 4010-4021页

作者： Solis, Adrian Rubio Panoutsos, George Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

granular computing is a computational paradigm that mimics human cognition in terms of grouping similar information together. Compatibility operators such as cardinality, orientation, density, and multidimensional length act on both in raw data and information granules which are formed from raw data providing a framework for human-like information processing where information granulation is intrinsic. granular computing, as a computational concept, is not new, however it is only relatively recent when this concept has been formalised computationally via the use of Computational Intelligence methods such as Fuzzy Logic and Rough Sets. Neutrosophy is a unifying field in logics that extents the concept of fuzzy sets into a three-valued logic that uses an indeterminacy value, and it is the basis of neutrosophic logic, neutrosophic probability, neutrosophic statistics and interval valued neutrosophic theory. In this paper we present a new framework for creating granular computing Neural-Fuzzy modelling structures via the use of Neutrosophic Logic to address the issue of uncertainty during the data granulation process. The theoretical and computational aspects of the approach are presented and discussed in this paper, as well as a case study using real industrial data. The case study under investigation is the predictive modelling of the Charpy Toughness of heat-treated steel;a process that exhibits very high uncertainty in the measurements due to the thermomechanical complexity of the Charpy test itself. The results show that the proposed approach leads to more meaningful and simpler granular models, with a better generalisation performance as compared to other recent modelling attempts on the same data set. (C) 2012 Elsevier B. V. All rights reserved.

关键词： granular computing Information granulation Neural-fuzzy logic Neutrosophic logic Charpy toughness

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granular computing Techniques for Classification and Semantic Characterization of Structured Data

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COGNITIVE COMPUTATION 2016年第3期8卷 442-461页

作者： Bianchi, Filippo Maria Scardapane, Simone Rizzi, Antonello Uncini, Aurelio Sadeghian, Alireza SAPIENZA Univ Rome Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy Ryerson Univ Dept Comp Sci 350 Victoria St Toronto ON M5B 2K3 Canada

We propose a system able to synthesize automatically a classification model and a set of interpretable decision rules defined over a set of symbols, corresponding to frequent substructures of the input dataset. Given a preprocessing procedure which maps every input element into a fully labeled graph, the system solves the classification problem in the graph domain. The extracted rules are then able to characterize semantically the classes of the problem at hand. The structured data that we consider in this paper are images coming from classification datasets: they represent an effective proving ground for studying the ability of the system to extract interpretable classification rules. For this particular input domain, the preprocessing procedure is based on a flexible segmentation algorithm whose behavior is defined by a set of parameters. The core inference engine uses a parametric graph edit dissimilarity measure. A genetic algorithm is in charge of selecting suitable values for the parameters, in order to synthesize a classification model based on interpretable rules which maximize the generalization capability of the model. Decision rules are defined over a set of information granules in the graph domain, identified by a frequent substructures miner. We compare the system with two other state-of-the-art graph classifiers, evidencing both its main strengths and limits.

关键词： granular computing Automatic semantic interpretation Frequent substructures miner Graph matching Graph classification Evolutionary optimization Watershed segmentation

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