A Classification Method for Imbalanced Data Based on Ant Lion Optimizer

Li, Mengmeng; Liu, Yi; Zheng, Qibin; Li, Xiang; Qin, Wei

doi:10.1007/978-981-19-9297-1_26

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…SMOTE algorithm is an oversampling method for synthesizing minority class samples, the basic idea of which is to analyze the minority class samples and synthesize new samples based on the minority class samples and add them to the dataset, thus alleviating the problems caused by unbalanced data [9]. Steps for synthesizing new samples:…”

Section: Smote Algorithmmentioning

confidence: 99%

A Fraud Detection Method for Online Payment Transactions Based on Deep Learning

Cui,

Li,

Song

2024

Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29,

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With the rapid development of the Internet, e-commerce and internet finance have achieved rapid development, but it has also brought serious problems of online payment risk fraud. Among them, the imbalance of financial data and the accuracy of online fraud detection are the main problems. Processing the original data and optimizing the algorithm model are important means to improve the accuracy of online fraud detection of unbalanced financial data. Among them, SMOTE algorithm, deep learning model, and mixed sampling algorithm are widely used. The deep learning model improves the accuracy of online fraud detection by improving the network structure. This article proposes an online transaction fraud detection method based on deep learning and SMOTE algorithm, which synthesizes minority class samples using the SMOTE algorithm and extracts data features using deep learning models to solve the problems of data imbalance and low fraud detection accuracy. This method is expected to provide more reliable guarantees for relevant industries, assist financial institutions and online payment platforms in timely and effective fraud detection and prevention, and provide guidance and suggestions for industry behavior.

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Section: Smote Algorithmmentioning

confidence: 99%

A Fraud Detection Method for Online Payment Transactions Based on Deep Learning

Cui,

Li,

Song

2024

Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29,

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Metaheuristic-based ensemble learning: an extensive review of methods and applications

Rezk,

Selim

2024

Neural Comput & Applic

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Ensemble learning has become a cornerstone in various classification and regression tasks, leveraging its robust learning capacity across disciplines. However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles.

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A Classification Method for Imbalanced Data Based on Ant Lion Optimizer

Cited by 2 publications

References 42 publications

A Fraud Detection Method for Online Payment Transactions Based on Deep Learning

A Fraud Detection Method for Online Payment Transactions Based on Deep Learning

Metaheuristic-based ensemble learning: an extensive review of methods and applications

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