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Optimization methods applied to neural networks for anomaly detection in credit card transactions

Grant number: 19/13420-4
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): September 01, 2019
Effective date (End): August 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Mathematics
Principal Investigator:Luis Felipe Cesar da Rocha Bueno
Grantee:Frederico José Ribeiro Pelogia
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Associated research grant:18/24293-0 - Computational methods in optimization, AP.TEM


In this project, it is intended to study optimization methods, especially stochastic algorithms, applied to the training of neural networks. In a recent paper proposed by BUENO, L. F. and MARTÍNES,J. M., "On the complexity of solving feasibility problems", 2018, a first-order algorithm was presented with good complexity results for least squares problems. One of this project's research main point is to develop a stochastic version of this algorithm. The performance of the studied algorithms will be analyzed when applied to fraud detection in credit card operations using Kaggle's Credit Card Fraud Detection database.