关于信用卡欺诈检测的机器学习技术和用户证实方法的广泛调查

Q3 Decision Sciences

International Journal of Business Intelligence and Data Mining Pub Date : 2023-01-01 DOI:10.1504/ijbidm.2023.127325

T. J. Berkmans, S. Karthick

引用次数: 0

摘要

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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A widespread survey on machine learning techniques and user substantiation methods for credit card fraud detection

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来源期刊

International Journal of Business Intelligence and Data Mining Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

1.50

自引率

0.00%

发文量

期刊介绍： IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. IJBIDM highlights intelligent techniques used for business modelling, including all areas of data visualisation, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, data mining techniques, tools and applications, neurocomputing, evolutionary computing, fuzzy techniques, expert systems, knowledge filtering, and post-processing. Topics covered include Data extraction/reporting/cleaning/pre-processing OLAP, decision analysis, causal modelling Reasoning under uncertainty, noise in data Business intelligence cycle Model specification/selection/estimation Web technology, mining, agents Fuzzy, neural, evolutionary approaches Genetic algorithms, machine learning, expert/hybrid systems Bayesian inference, bootstrap, randomisation Exploratory/automated data analysis Knowledge-based analysis, statistical pattern recognition Data mining algorithms/processes Classification, projection, regression, optimisation clustering Information extraction/retrieval, human-computer interaction Multivariate data visualisation, tools.