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Ministério da Defesa (Brasil). Instituto Tecnológico de Aeronáutica (ITA). Divisão de Ciência da Computação (IEC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Graduate at Computer Science from Universidade de São Paulo (2001) and PhD at Computer Science from Universidade de São Paulo - São Carlos (2006). Professor at Instituto Tecnológico de Aeronáutica (ITA). Has experience in Computer Science, focusing on Artificial Intelligence, acting on the following subjects: supervised Machine Learning, Data Mining, Data Science. (Source: Lattes Curriculum)
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The area of Meta-learning (MtL) leverages onknowledge from problems for which successful Machine Learning (ML) solutions are known to support automated algorithm selection for new problems. But far more meta-knowledge can be extracted by relating data properties to algorithmic performance, a topic which remains under-explored compared to the usage of MtL for automated algorithm selection.…
Machine Learning (ML) techniques have been successfully employed in the solution of various data classification problems. Recently some studies are devoted to understand how quantitative measures quantifying the complexity of data sets used for classification, such as the geometric overlap between classes, affects the performance of ML techniques. Among the contributions of this approach …
Real-world data often present challenges such as imbalance, noise, and missing values. Recently, the Machine Learning literature has demonstrated that improving model performance also requires enhancing the quality of the data used for training, giving rise to a new research field known as Data-Centric Artificial Intelligence. Missing values, defined as the absence of information in one o…
Transfer learning is a popular strategy to use knowledge extracted from a given source domain to solve another target task. There are many strategies for transfer learning, from sampling part of the data from the source domain to using models induced in a source domain as starting solutions to solve the target tasks. This work will investigate the issue of choosing which source domain can…
The OpenML repository is widely employed by the Machine Learning (ML) community, storing a large number of datasets along with the results of com-putational experiments run on the same data. Some meta-analysis of ML datasets are also provided in the MATILDA repository (Melbourne Algorithm Test Instance Library with Data Analytics). The objective of this work is to analyze meta-datasetsgen…
(Only some records are available in English at this moment)
On-line platforms such as Kaggle have been used by companies to launch data analytics challenges. The objective of this work is to generate a meta-dataset in the attribute-value format which gathers characteristics of datasets from some of the competitions and also the performance achieved by a set of techniques in their solution. Finally, the difficulty level of the challenges will be as…
The OpenML repository is widely employed by the Machine Learning (ML) community, storing a large number of datasets along with the results of computational experiments run on the same data. The objective of this work is to generate a meta-dataset in the attribute-value format joining the characteristics of the OpenML datasets and also the performance achieved by different ML techniques in…
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 v…
(Only some records are available in English at this moment)
Machine learning has undoubtedly transformed computer science research, promoting significant technological advances by enabling computers to learn from data, make decisions and recognise patterns autonomously. Although numerous ML algorithms are available, it has been shown that no single algorithm is superior across all possible datasets. Therefore, an effective algorithm selection proc…
Machine learning (ML) approaches have become a vastly studied solution for challenging problems that are difficult to quantify or solve using traditional methods. One of the first challenges on solving a ML problem is determining which algorithm best fits its instance domain. Since no single ML algorithm can achieve optimal performance across every classification problems, choosing the r…
Various issues can deteriorate data quality in Machine Learning. Among them, one may cite the quality of the input features, which can be impaired by the presence of noise and missing data. Noise can be inputted into data in several steps during collection, storage, and transmission. In supervised problems, they can be present both in the labels and in the predictive input features. Missi…
(Only some records are available in English at this moment)
| 1 / 1 | Ongoing grants |
| 5 / 3 | Completed research grants |
| 5 / 5 | Ongoing scholarships in Brazil |
| 10 / 8 | Completed scholarships in Brazil |
| 7 / 7 | Completed scholarships abroad |
| 28 / 24 | All research grants and scholarships |
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