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Novelty detection by machine learning

Grant number: 15/03355-0
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): August 01, 2015
Effective date (End): July 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:André Carlos Ponce de Leon Ferreira de Carvalho
Grantee:Isvani Inocencio Frías Blanco
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID


Traditional machine learning classification techniques are essentially based on the premise that the future distribution of the data remains consistent to the distribution presented in the training set, i.e.,the data distribution is stationary along time. However, over the past few years, more and more new applications need to process not only increasing volumes of data, but also data that is presented continually as a stream. In this scenario the aforementioned assumption is rarely satisfied; future data distribution often changes while data stream processing demands high costs to traditional algorithms, such of processing time and memory. A particular classification problem, which results from such non-stationary datastream, regards identifying previously unknown classes over the stream- a task known as novelty detection. Facing this problem, novelty detection algorithms must conciliate essential, but difficult to gather features, like accuracy and stability at classification performance, processing data instances only once and providing low associated costs with processing time and memory. This work aims at investigating the state of the art methods on novelty detections as well as proposing new ones. The studied and developed methods should be applied to real industrial applications, such as in monitoring and controlling tasks.

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)
VERDECIA-CABRERA, ALBERTO; BLANCO, ISVANI FRIAS; CARVALHO, ANDRE C. P. L. F. An online adaptive classifier ensemble for mining non-stationary data streams. Intelligent Data Analysis, v. 22, n. 4, p. 787-806, 2018. Web of Science Citations: 0.
FRIAS-BLANCO, ISVANI; DEL CAMPO-AVILA, JOSE; RAMOS-JIMENEZ, GONZALO; CARVALHO, ANDRE C. P. L. F.; ORTIZ-DIAZ, AGUSTIN; MORALES-BUENO, RAFAEL. Online adaptive decision trees based on concentration inequalities. KNOWLEDGE-BASED SYSTEMS, v. 104, p. 179-194, JUL 15 2016. Web of Science Citations: 3.

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