IEEE-CIS fraud detection: a case study for fraudulent transaction detection based on supervised learning models

ABSTRACT : This paper proposes a solution to the Kaggle competition: "IEE-Fraud Detection", whose objective is to detect fraudulent transactions in a customer and transactional dataset collected by an E-commerce site to construct a transaction confirmation system via text messaging of the...

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Autores:
González Benaissa, Aarón Al Rachid
Tipo de recurso:
Tesis
Fecha de publicación:
2021
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/20175
Acceso en línea:
http://hdl.handle.net/10495/20175
https://github.com/AaronGonzalezB/monografia-especializacion-udea.git
Palabra clave:
Electronic commerce
Comercio electrónico
Artificial intelligence
Inteligencia artificial
Fraud
Fraude
Illegal practices
Practicas Ilegales
Classification systems
Sistemas de Clasificación
Linked open data
Datos abiertos vinculados
Fraud detection
binary classification
imbalanced data
dimensionality reduction
http://aims.fao.org/aos/agrovoc/c_8139c3d0
http://aims.fao.org/aos/agrovoc/c_15682
http://aims.fao.org/aos/agrovoc/c_9000017
http://aims.fao.org/aos/agrovoc/c_773acdb4
http://vocabularies.unesco.org/thesaurus/concept11036
http://vocabularies.unesco.org/thesaurus/concept3052
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-sa/2.5/co/