An unified approach for dealing with missing and noise data

• Grant number: 22/10553-6
• Support Opportunities: Scholarships in Brazil - Master
• Start date: October 01, 2022
• End date: February 28, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Ana Carolina Lorena
• Grantee: Arthur Dantas Mangussi
• Host Institution: Divisão de Ciência da Computação (IEC). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
• Associated research grant: 21/06870-3 - Beyond algorithm selection: meta-learning for data and algorithm analysis and understanding, AP.JP2
• Associated scholarship(s): 23/13688-2 - Um modelo Autoencoder para lidar com dados ausentes e com ruído, BE.EP.MS

Abstract

Various issues can deteriorate data quality in Machine Learning. Among them, one may cite the quality of the features, which can be impaired by the presence of noise and missing data. Noise can be inputted in data in several steps during data collection, storage and transmission. In supervised problems, they can be present both in the labels and in the predictive input features. Missing values are also a concern, as other information about the data item can be important and must be regarded. This project will investigate these two problems and strategies to deal with them, by taking possible corrective approaches.

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)

MANGUSSI, ARTHUR DANTAS; SANTOS, MIRIAM SEOANE; LOPES, FILIPE LOYOLA; PEREIRA, RICARDO CARDOSO; LORENA, ANA CAROLINA; ABREU, PEDRO HENRIQUES. mdatagen: A python library for the artificial generation of missing data. Neurocomputing, v. 625, p. 10-pg., 2025-01-29. (23/13688-2, 21/06870-3, 22/10553-6)