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Sadreddin A, Sadaoui S. Chunk-based incremental feature learning for credit-card fraud data stream. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2153277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Armin Sadreddin
- Department of Computer Science, University of Regina, Regina, SK, Canada
| | - Samira Sadaoui
- Department of Computer Science, University of Regina, Regina, SK, Canada
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Distance-based arranging oversampling technique for imbalanced data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07828-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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3
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Tian Y, Zhao X, Huang W. Meta-learning approaches for learning-to-learn in deep learning: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Al-Badarneh I, Habib M, Aljarah I, Faris H. Neuro-evolutionary models for imbalanced classification problems. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.11.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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5
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Application of loan lost-linking customer path correlated index model and network sorting search algorithm based on big data environment. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07189-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Cost-Sensitive Broad Learning System for Imbalanced Classification and Its Medical Application. MATHEMATICS 2022. [DOI: 10.3390/math10050829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
As an effective and efficient discriminative learning method, the broad learning system (BLS) has received increasing attention due to its outstanding performance without large computational resources. The standard BLS is derived under the minimum mean square error (MMSE) criterion, while MMSE is with poor performance when dealing with imbalanced data. However, imbalanced data are widely encountered in real-world applications. To address this issue, a novel cost-sensitive BLS algorithm (CS-BLS) is proposed. In the CS-BLS, many variations can be adopted, and CS-BLS with weighted cross-entropy is analyzed in this paper. Weighted penalty factors are used in CS-BLS to constrain the contribution of each sample in different classes. The samples in minor classes are allocated higher weights to increase their contributions. Four different weight calculation methods are adopted to the CS-BLS, and thus, four CS-BLS methods are proposed: Log-CS-BLS, Lin-CS-BLS, Sqr-CS-BLS, and EN-CS-BLS. Experiments based on artificially imbalanced datasets of MNIST and small NORB are firstly conducted and compared with the standard BLS. The results show that the proposed CS-BLS methods have better generalization and robustness than the standard BLS. Then, experiments on a real ultrasound breast image dataset are conducted, and the results demonstrate that the proposed CS-BLS methods are effective in actual medical diagnosis.
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G. Martín A, Fernández-Isabel A, Martín de Diego I, Beltrán M. A survey for user behavior analysis based on machine learning techniques: current models and applications. APPL INTELL 2021. [DOI: 10.1007/s10489-020-02160-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Gnip P, Vokorokos L, Drotár P. Selective oversampling approach for strongly imbalanced data. PeerJ Comput Sci 2021; 7:e604. [PMID: 34239981 PMCID: PMC8237317 DOI: 10.7717/peerj-cs.604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/31/2021] [Indexed: 06/03/2023]
Abstract
Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most representative samples from minority classes by using an outlier detection technique and then utilizes these samples for synthetic oversampling. We show that the proposed approach improves the performance of two state-of-the-art oversampling methods, namely, the synthetic minority oversampling technique and adaptive synthetic sampling. The prediction performance is evaluated on four synthetic datasets and four real-world datasets, and the proposed SOA methods always achieved the same or better performance than other considered existing oversampling methods.
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Affiliation(s)
- Peter Gnip
- Department of Computers and Informatics, Technical University of Košice, Slovak Republic
| | - Liberios Vokorokos
- Department of Computers and Informatics, Technical University of Košice, Slovak Republic
| | - Peter Drotár
- Department of Computers and Informatics, Technical University of Košice, Slovak Republic
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Farrugia D, Zerafa C, Cini T, Kuasney B, Livori K. A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry. SN COMPUTER SCIENCE 2021; 2:215. [PMID: 33880451 PMCID: PMC8049394 DOI: 10.1007/s42979-021-00623-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/27/2021] [Indexed: 11/24/2022]
Abstract
This paper presents a real-time fully autonomous prescriptive solution for explainable cyber-fraud detection within the iGaming industry. We demonstrate how our solution facilitates the time-consuming task of player risk and fraud assessment through prescriptive analytics. Our tool leverages machine learning algorithms and advancements in the field of eXplainable AI to derive smarter predictions empowered by local interpretable explanations in real-time. Our best-performing pipeline was able to predict fraudulent behaviour with an average precision of 84.2% and an area under the receiver operating characteristics of 0.82 on our dataset. We also addressed the phenomenon of concept-drift and discussed our empirical and data-driven strategy for detecting and dealing with this problem. Finally, we cover how local interpretable explanations can help adopt a pro-active stance in fighting fraud.
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Affiliation(s)
| | | | - Tony Cini
- Gaming Innovation Group, St. Julians, Malta
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Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams. SMART CITIES 2021. [DOI: 10.3390/smartcities4010021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.
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Zhou R, Zhang Q, Zhang P, Niu L, Lin X. Anomaly detection in dynamic attributed networks. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05091-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Stojanović B, Božić J, Hofer-Schmitz K, Nahrgang K, Weber A, Badii A, Sundaram M, Jordan E, Runevic J. Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications. SENSORS 2021; 21:s21051594. [PMID: 33668773 PMCID: PMC7956727 DOI: 10.3390/s21051594] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 11/18/2022]
Abstract
Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.
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Affiliation(s)
- Branka Stojanović
- Joanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, Austria; (J.B.); (K.H.-S.); (K.N.)
- Correspondence:
| | - Josip Božić
- Joanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, Austria; (J.B.); (K.H.-S.); (K.N.)
| | - Katharina Hofer-Schmitz
- Joanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, Austria; (J.B.); (K.H.-S.); (K.N.)
| | - Kai Nahrgang
- Joanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, Austria; (J.B.); (K.H.-S.); (K.N.)
| | - Andreas Weber
- Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, D-79588 Efringen-Kirchen, Germany;
| | - Atta Badii
- Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UK; (A.B.); (M.S.); (E.J.); (J.R.)
| | - Maheshkumar Sundaram
- Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UK; (A.B.); (M.S.); (E.J.); (J.R.)
| | - Elliot Jordan
- Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UK; (A.B.); (M.S.); (E.J.); (J.R.)
| | - Joel Runevic
- Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UK; (A.B.); (M.S.); (E.J.); (J.R.)
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