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Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
graduation at Ciência da Computação from Universidade Estadual Paulista Júlio de Mesquita Filho (2012), master's at Computer Science from Universidade de São Paulo (2015) and doctorate at Computer Science from Universidade de São Paulo (2021). Has experience in Computer Science, focusing on Machine Learning, acting on the following subjects: imbalanced data, data complexity measures, dados desbalanceados, classification and sampling. (Source: Lattes Curriculum)
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The analysis and prediction of crimes in urban environments pose significant challenges from a data science perspective. These challenges encompass temporal data dependencies, the inherent street graph structure, high granularity (down to street segment levels), and the sparse nature of data in both time and space. This project focuses on investigating and implementing a predictive model …
The pre-processing of data is one of the most important steps in the data mining process, and one of the most neglected. Data collection may suffer from manual errors and equipment problems creating inconsistent, noisy or missing data. There are some other aspects, as imbalance and overlapping classes, which may difficult the analysis. Ignoring these aspects in the learning process can im…
Many key machine learning algorithms can not perform well for classification in scenarios in which there is disproportion between the quantities of examples from different classes. This problem is known as unbalanced data (or imbalanced classes), which is the subject of this project. Among the challenges of working with such databases is dealing with distinct distributions between groups …
Several works have investigated the effect of data imbalance on the performance of predictive models. Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalance data poses a difficulty for the minority class whe…
| 1 / 1 | Ongoing scholarships in Brazil |
| 3 / 3 | Completed scholarships in Brazil |
| 1 / 1 | Completed scholarships abroad |
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