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Exploring Multi-labeling approaches by Optimum-Path Forest

Grant number: 11/14094-1
Support type:Scholarships in Brazil - Master
Effective date (Start): March 01, 2012
Effective date (End): February 28, 2014
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:João Paulo Papa
Grantee:Luis Augusto Martins Pereira
Home Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
Associated research grant:09/16206-1 - New trends on optimum-path forest-based pattern recognition, AP.JP


Traditional pattern recognition techniques associate one label to each dataset sample considering aprevious knowledge obtained from a training dataset, which can be labeled or not. In the first case,each sample can be labeled automatically or manually by a technician. However, in many situations,a given sample can be labeled by more than one label. Medical diagnostic systems, for example,can diagnose more than one disease for a patient. Applications of musical recommendation, widelyused in social networks, can be trained using labels offered by user in a subjective way. Otherinteresting application concerns with the classification of documents, which can be associated tomany classes at the same time, such as religion and theater, for example. In this context, the present research projectproposes to study several multi-label methods and develop a multi-label method using theOptimum-Path Forest classifier, which was recently proposed in the literature and has not been employed to thispurpose so far. This project also proposes to include the algorithms generated here in the LibOPF, which is an open sourcelibrary written in C Programming Language and aims the development of classifiers based on optimum-path forest. As new versions of the library will be published, this project will contribute for new functionalities of its. Some datasets will be used to validate the work.

Scientific publications (4)
(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)
PEREIRA, LUIS A. M.; PAPA, JOAO P.; COELHO, ANDRE L. V.; LIMA, CLODOALDO A. M.; PEREIRA, DANILLO R.; DE ALBUQUERQUE, VICTOR HUGO C. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. NEURAL COMPUTING & APPLICATIONS, v. 31, n. 2, p. 1317-1329, FEB 2019. Web of Science Citations: 0.
PIRES, RAFAEL G.; PEREIRA, DANILLO R.; PEREIRA, LUIS A. M.; MANSANO, ALEX F.; PAPA, JOO P. Projections onto convex sets parameter estimation through harmony search and its application for image restoration. NATURAL COMPUTING, v. 15, n. 3, SI, p. 493-502, SEP 2016. Web of Science Citations: 3.
COSTA, KELTON A. P.; PEREIRA, LUIS A. M.; NAKAMURA, RODRIGO Y. M.; PEREIRA, CLAYTON R.; PAPA, JOAO P.; FALCAO, ALEXANDRE XAVIER. A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks. INFORMATION SCIENCES, v. 294, p. 95-108, FEB 10 2015. Web of Science Citations: 30.
PEREIRA, LUIS A. M.; NAKAMURA, RODRIGO Y. M.; DE SOUZA, GUILHERME F. S.; MARTINS, DAGOBERTO; PAPA, JOAO P. Aquatic weed automatic classification using machine learning techniques. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 87, p. 56-63, SEP 2012. Web of Science Citations: 12.
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
PEREIRA, Luis Augusto Martins. Explorando abordagens de múltiplos rótulos por floresta de caminhos ótimos. 2013. 64 f. Master's Dissertation - Universidade Estadual Paulista "Júlio de Mesquita Filho" Instituto de Biociências, Letras e Ciências Exatas..

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