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An unified approach for dealing with missing and noise data

Grant number: 22/10553-6
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): October 01, 2022
Effective date (End): September 30, 2024
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


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.

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