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Noise Labels in Machine Learning: Evaluation measures and machine learning algorithms

Grant number: 15/20606-6
Support type:Scholarships abroad - Research
Effective date (Start): April 01, 2016
Effective date (End): March 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Ronaldo Cristiano Prati
Grantee:Ronaldo Cristiano Prati
Host: Francisco Herrera
Home Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
Local de pesquisa : Universidad de Granada (UGR), Spain  


Supervised Machine Learning aims to automatically build classification models from a set o labeled data. However, highly quality labeled data is not available in numerous real world problems, an issue known as label noise. This label noise may cause numerous problems for Machine Learning algorithms. Although some research has been conducted in the model generation phase, few studies focus on the evaluation process in presence of noise labels. This is an important research topic, as Machine Learning research is based on empirical studies, and misleading conclusions may be drawn when comparing learning algorithms in class noise context. This research proposal aims to investigate this open-ended topic, developing a systematic study regarding the influence of some class-noise patterns in the evaluation phase of the machine learning process.

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)
FARIAS, DELIA IRAZU HERNANDEZ; PRALI, RONALDO; HERRERA, FRANCISCO; ROSSO, PAOLO. Irony detection in Twitter with imbalanced class distributions. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, v. 39, n. 2, p. 2147-2163, 2020. Web of Science Citations: 0.
PRATI, RONALDO C.; LUENGO, JULIAN; HERRERA, FRANCISCO. Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. KNOWLEDGE AND INFORMATION SYSTEMS, v. 60, n. 1, p. 63-97, JUL 2019. Web of Science Citations: 0.

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