FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387427279> ?p ?o ?g. }

• W4387427279 endingPage "103618" @default.
• W4387427279 startingPage "103618" @default.
• W4387427279 abstract "Electronic payment methods have become increasingly popular for business transactions, both online and in-person, across the globe. Anomalies like online fraud and default payments, which can result in substantial financial losses, have become more common as the usage of credit cards in online purchases has increased. To address this issue, researchers have explored various machine learning models and their ensemble techniques for detecting anomalies in credit card transaction data. However, detecting anomalies in this data can be challenging due to overlapping class samples and an imbalanced class distribution. Therefore, the detection rate of anomalies from minority class samples is relatively low, and general learning algorithms can be biased towards the majority class samples. In this paper, we propose a model called Credit Card Anomaly Detection (CCAD) that leverages the base learners paradigm and meta-learning ensemble techniques to improve the detection rate of credit card anomalies. We utilize four outlier detection algorithms as base learners and XGBoost algorithm as meta learner in the proposed stacked ensemble approach to detect anomaly in credit card transactions. We apply stratified sampling technique and k-fold cross-validation process to address the issues of data imbalance and overfitting. In addition, the discordance rate is calculated to enhance the accuracy of ensemble learning performances. The proposed model is trained and tested using two datasets: CCF (Credit Card Fraud) and CCDP (Credit Card Default Payment). Experimental results demonstrate that our approach outperforms existing approaches, particularly in detecting anomalies from the minority class instances of these datasets." @default.
• W4387427279 created "2023-10-08" @default.
• W4387427279 creator A5008205801 @default.
• W4387427279 creator A5038741954 @default.
• W4387427279 creator A5065107822 @default.
• W4387427279 creator A5087848827 @default.
• W4387427279 date "2023-11-01" @default.
• W4387427279 modified "2023-10-18" @default.
• W4387427279 title "An ensemble learning approach for anomaly detection in credit card data with imbalanced and overlapped classes" @default.
• W4387427279 cites W2003303386 @default.
• W4387427279 cites W2032435122 @default.
• W4387427279 cites W2045049630 @default.
• W4387427279 cites W2118978333 @default.
• W4387427279 cites W2122646361 @default.
• W4387427279 cites W2132870739 @default.
• W4387427279 cites W2158698691 @default.
• W4387427279 cites W2191253925 @default.
• W4387427279 cites W2571178338 @default.
• W4387427279 cites W2586297576 @default.
• W4387427279 cites W2763619424 @default.
• W4387427279 cites W2775447965 @default.
• W4387427279 cites W2785637175 @default.
• W4387427279 cites W2786577118 @default.
• W4387427279 cites W2789081542 @default.
• W4387427279 cites W2839737605 @default.
• W4387427279 cites W2930363294 @default.
• W4387427279 cites W2944842185 @default.
• W4387427279 cites W2945876440 @default.
• W4387427279 cites W2983029853 @default.
• W4387427279 cites W3000795845 @default.
• W4387427279 cites W3004689489 @default.
• W4387427279 cites W3006682208 @default.
• W4387427279 cites W3034143844 @default.
• W4387427279 cites W3040219594 @default.
• W4387427279 cites W3095164600 @default.
• W4387427279 cites W3102476541 @default.
• W4387427279 cites W3122007073 @default.
• W4387427279 cites W3126232929 @default.
• W4387427279 cites W4212968553 @default.
• W4387427279 cites W4225146313 @default.
• W4387427279 cites W4254182148 @default.
• W4387427279 cites W435157458 @default.
• W4387427279 cites W834036986 @default.
• W4387427279 doi "https://doi.org/10.1016/j.jisa.2023.103618" @default.
• W4387427279 hasPublicationYear "2023" @default.
• W4387427279 type Work @default.
• W4387427279 citedByCount "0" @default.
• W4387427279 crossrefType "journal-article" @default.
• W4387427279 hasAuthorship W4387427279A5008205801 @default.
• W4387427279 hasAuthorship W4387427279A5038741954 @default.
• W4387427279 hasAuthorship W4387427279A5065107822 @default.
• W4387427279 hasAuthorship W4387427279A5087848827 @default.
• W4387427279 hasBestOaLocation W43874272791 @default.
• W4387427279 hasConcept C119857082 @default.
• W4387427279 hasConcept C124101348 @default.
• W4387427279 hasConcept C127722929 @default.
• W4387427279 hasConcept C136764020 @default.
• W4387427279 hasConcept C145097563 @default.
• W4387427279 hasConcept C154945302 @default.
• W4387427279 hasConcept C22019652 @default.
• W4387427279 hasConcept C2777212361 @default.
• W4387427279 hasConcept C2780747020 @default.
• W4387427279 hasConcept C2983355114 @default.
• W4387427279 hasConcept C41008148 @default.
• W4387427279 hasConcept C45942800 @default.
• W4387427279 hasConcept C50644808 @default.
• W4387427279 hasConcept C739882 @default.
• W4387427279 hasConcept C75949130 @default.
• W4387427279 hasConcept C77088390 @default.
• W4387427279 hasConcept C79337645 @default.
• W4387427279 hasConceptScore W4387427279C119857082 @default.
• W4387427279 hasConceptScore W4387427279C124101348 @default.
• W4387427279 hasConceptScore W4387427279C127722929 @default.
• W4387427279 hasConceptScore W4387427279C136764020 @default.
• W4387427279 hasConceptScore W4387427279C145097563 @default.
• W4387427279 hasConceptScore W4387427279C154945302 @default.
• W4387427279 hasConceptScore W4387427279C22019652 @default.
• W4387427279 hasConceptScore W4387427279C2777212361 @default.
• W4387427279 hasConceptScore W4387427279C2780747020 @default.
• W4387427279 hasConceptScore W4387427279C2983355114 @default.
• W4387427279 hasConceptScore W4387427279C41008148 @default.
• W4387427279 hasConceptScore W4387427279C45942800 @default.
• W4387427279 hasConceptScore W4387427279C50644808 @default.
• W4387427279 hasConceptScore W4387427279C739882 @default.
• W4387427279 hasConceptScore W4387427279C75949130 @default.
• W4387427279 hasConceptScore W4387427279C77088390 @default.
• W4387427279 hasConceptScore W4387427279C79337645 @default.
• W4387427279 hasFunder F4320320970 @default.
• W4387427279 hasFunder F4320321873 @default.
• W4387427279 hasFunder F4320338279 @default.
• W4387427279 hasLocation W43874272791 @default.
• W4387427279 hasOpenAccess W4387427279 @default.
• W4387427279 hasPrimaryLocation W43874272791 @default.
• W4387427279 hasRelatedWork W2483711049 @default.
• W4387427279 hasRelatedWork W2984276143 @default.
• W4387427279 hasRelatedWork W3150316110 @default.
• W4387427279 hasRelatedWork W3153799676 @default.
• W4387427279 hasRelatedWork W3157031617 @default.

• next