CV Lattes
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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
Newton Spolaôr finished a postdoctorate at the Western Paraná State University - UNIOESTE (2016). He also holds a Ph.D. degree in Computer Science at the Institute of Mathematics and Computer Science from the University of São Paulo, Brazil (2014). He also holds an M.Sc. degree in Information Engineering from the Federal University of ABC (2010) and a B.Sc. degree in javascript:void(0)Computer Science from the UNIOESTE (2008). He has carried out a research internship at Aristotle University of Thessaloniki in Greece (2013). Currently, he is volunteer researcher at the Laboratory of Bioinformatics at UNIOESTE and full-time temporary lecturer at UNIOESTE. He was also lecturer at CESUFOZ and UniAmérica. Newton has experience in Computer Science, acting mainly on the following subjects: single-label and multi-label feature selection, time series clustering, medical image analysis, knowledge discovery in databases and mapping of medical data into structured databases. He also has experience in systematic review application on machine learning and educational robotics. In addition, he has worked as reviewer for scientific journals and conferences in Brazil and abroad. (Source: Lattes Curriculum)
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The occurrence of irrelevant and/or redundant attributes in databases may impair the performance of computational processes for knowledge discovery, which motivates the application of feature selection techniques. The combinatorial nature of this problem makes the use of heuristics methods such as genetic algorithms appropriate, in order to obtain or approximate optimal subset of attribut…
Feature Selection (FS) is an important task in machine learning, which caneffectively reduce the dataset dimensionality by removing irrelevantand/or redundant features. Although a large body of research dealswith FS in single-label data, in which several measures have been proposedto filter out irrelevant features, this is not the case for multi-labeldata where each instance is la…
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