Classification of non-stationary data stream with application in sensors for insect identification

• Grant number: 11/17698-5
• Support type: Scholarships in Brazil - Doctorate
• Effective date (Start): September 01, 2012
• Effective date (End): February 29, 2016
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal researcher: Gustavo Enrique de Almeida Prado Alves Batista
• Grantee: Vinícius Mourão Alves de Souza
• Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 13/23037-7 - Data stream classification in the presence of concept drifts using non-supervised methods, BE.EP.DR

Abstract

Applications such as intelligent sensors should be able to collect environment information and make decisions based on input data. An example is an under-development low-cost sensor to detect and classify insects in their species using a laser beam and machine learning techniques. This sensor is an important step towards the development of intelligent traps capable of attracting and selectively capturing insect species of interest such as disease vectors or agricultural pests, without affecting the beneficial species. The data gathered by the sensor constitutes a stream with non-stationary characteristics in which the main information is the insect wing beat frequency and it is influenced by environmental conditions like temperature, humidity and atmospheric pressure. The sensor classification algorithm should be capable of identifying concept drifts in absence of labels in the test phase, differently from the current techniques. Furthermore, being an embedded system, the sensor has memory and processing constraints. Thus, the main objective of this work is the classification of non-stationary data streams without the need of immediate availability of true class labels for the last classified instance. This classification should be efficient in terms of memory and processing to be embedded in a sensor.

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)

SOUZA, VINICIUS M. A.; ROSSI, RAFAEL G.; BATISTA, GUSTAVO E. A. P. A.; REZENDE, SOLANGE O. Unsupervised active learning techniques for labeling training sets: An experimental evaluation on sequential data. Intelligent Data Analysis, v. 21, n. 5, p. 1061+, 2017. Web of Science Citations: 0.

SILVA, DIEGO F.; SOUZA, VINICIUS M. A.; ELLIS, DANIEL P. W.; KEOGH, EAMONN J.; BATISTA, GUSTAVO E. A. P. A. Exploring Low Cost Laser Sensors to Identify Flying Insect Species Evaluation of Machine Learning and Signal Processing Methods. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 80, n. 1, SI, p. S313-S330, DEC 2015. Web of Science Citations: 16.

SILVA, DIEGO FURTADO; ALVES DE SOUZA, VINICIUS MOURAO; PRADO ALVES BATISTA, GUSTAVO ENRIQUE DE ALMEIDA. A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English. ACTA SCIENTIARUM-TECHNOLOGY, v. 35, n. 4, p. 621-628, 2013. Web of Science Citations: 2.

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

. Classification of non-stationary data stream with application in sensors for insect identification.. 2016. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.