Version 1
: Received: 27 June 2024 / Approved: 27 June 2024 / Online: 27 June 2024 (17:03:40 CEST)
How to cite:
Zapata-Cortes, O.; Arango-Serna, M. D.; Zapata-Cortes, J. A.; Restrepo-Carmona, J. A. Machine Learning Models and Applications for Early Detection. Preprints2024, 2024061955. https://doi.org/10.20944/preprints202406.1955.v1
Zapata-Cortes, O.; Arango-Serna, M. D.; Zapata-Cortes, J. A.; Restrepo-Carmona, J. A. Machine Learning Models and Applications for Early Detection. Preprints 2024, 2024061955. https://doi.org/10.20944/preprints202406.1955.v1
Zapata-Cortes, O.; Arango-Serna, M. D.; Zapata-Cortes, J. A.; Restrepo-Carmona, J. A. Machine Learning Models and Applications for Early Detection. Preprints2024, 2024061955. https://doi.org/10.20944/preprints202406.1955.v1
APA Style
Zapata-Cortes, O., Arango-Serna, M. D., Zapata-Cortes, J. A., & Restrepo-Carmona, J. A. (2024). Machine Learning Models and Applications for Early Detection. Preprints. https://doi.org/10.20944/preprints202406.1955.v1
Chicago/Turabian Style
Zapata-Cortes, O., Julian Andres Zapata-Cortes and Jaime Alonso Restrepo-Carmona. 2024 "Machine Learning Models and Applications for Early Detection" Preprints. https://doi.org/10.20944/preprints202406.1955.v1
Abstract
From the various perspectives of Machine Learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the Early Detection (ED) of anoma-lies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision-making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLM) operate with a focus on ED in a multidisci-plinary manner and specifically how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVM), De-cision Trees (DT), Random Forests (RF), Naive Bayesian Classifier (NB), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBM) and Stacking Ensemble Models (SEM). It was identified that MLMs for ED in multiple areas under SBM and SEM implementation achieved accuracies greater than 80% and 90%, respectively. n fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.
Keywords
Machine Learning Models; Early Detection; Data Analysis; Fraud Detection; Performance Metrics; Stacking Ensemble
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
*
