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Exploring sequential learning approaches for Optimum-Path Forest

Grant number: 11/14058-5
Support type:Scholarships in Brazil - Master
Effective date (Start): March 01, 2012
Effective date (End): August 31, 2013
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:João Paulo Papa
Grantee:Rodrigo Yuji Mizobe Nakamura
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

Abstract

Traditional pattern recognition techniques assume that data is independent and identically distributed. Although in many real cases this is not true, in the context of intrusion detection, for instance, each sample of the dataset represents an access to a particular computer. The feature vector is created using TCP/IP packet information, such as source and destination address, packet size and permission to perform some process. Henceforth, it is necessary multiple accesses to the same machine to be classified as a particular attack. Thus, analyzing every individual sample may not be a very interesting alternative. In order to tackle this problem, sequential supervised learning techniques use spatial and/or temporal dependence to increase the effectiveness of traditional approaches. Among the many conventional ones, Optimum-Path Forest (OPF) was recently proposed in order to ally efficiency in the training phase and effectiveness in classification. Therefore, the present research work aims to study some sequential learning approaches and then to implement them with OPF. The main contribution of this work is to implement OPF classifier in the sequential supervised learning environment, since this task has not been done so far.

Scientific publications (5)
(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)
SAITO, PRISCILA T. M.; NAKAMURA, RODRIGO Y. M.; AMORIM, WILLIAN P.; PAPA, JOAO P.; DE REZENDE, PEDRO J.; FALCAO, ALEXANDRE X. Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint. PLoS One, v. 10, n. 6 JUN 26 2015. Web of Science Citations: 4.
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.
NAKAMURA, RODRIGO Y. M.; GARCIA FONSECA, LEILA MARIA; DOS SANTOS, JEFERSSON ALEX; TORRES, RICARDO DA S.; YANG, XIN-SHE; PAPA, JOAO PAPA. Nature-Inspired Framework for Hyperspectral Band Selection. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, v. 52, n. 4, p. 2126-2137, APR 2014. Web of Science Citations: 32.
PAPA, JOAO P.; NAKAMURA, RODRIGO Y. M.; DE ALBUQUERQUE, VICTOR HUGO C.; FALCAO, ALEXANDRE X.; TAVARES, JOAO MANUEL R. S. Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials. EXPERT SYSTEMS WITH APPLICATIONS, v. 40, n. 2, p. 590-597, FEB 1 2013. Web of Science Citations: 31.
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)
NAKAMURA, Rodrigo Yuji Mizobe. Explorando abordagens de aprendizado sequencial para floresta de caminhos ótimos. 2014. 55 f. Master's Dissertation - Universidade Estadual Paulista "Júlio de Mesquita Filho" Instituto de Biociencias, Letras e Ciencias Exatas..

Please report errors in scientific publications list by writing to: cdi@fapesp.br.