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

• Grant number: 19/13420-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2019
• End date: August 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Mathematics - Applied Mathematics
• Principal Investigator: Luis Felipe Cesar da Rocha Bueno
• Grantee: Frederico José Ribeiro Pelogia
• Host 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

Abstract

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.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BORTOLETE, J. C.; BUENO, L. F.; BUTKERAITES, R.; CHAVES, A. A.; COLLACO, G.; MAGUETA, M.; PELOGIA, F. J. R.; SALLES NETO, L. L.; SANTOS, T. S.; SILVA, T. S.; et al. A support tool for planning classrooms considering social distancing between students. COMPUTATIONAL & APPLIED MATHEMATICS, v. 41, n. 1, FEB 2022. (16/01860-1, 19/13420-4, 18/24293-0, 13/07375-0)