Data Stream Classification with Incremental Algorithms applied to Intelligent Sensors

• Grant number: 13/16130-0
• Support Opportunities: Scholarships in Brazil - Master
• Start date: October 01, 2013
• End date: February 28, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Gustavo Enrique de Almeida Prado Alves Batista
• Grantee: Luan Soares Oliveira
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Despite the biological importance, insects have always represented a threat to humans, both as agricultural, threatening crops, as in health, being transmitting agents of several diseases. The control of these insects is usually made with the aid of substances that are, many cases, harmful to humans and / or the environment. With the advancement in the data mining's field arise alternatives to these traditional methods, such as the use of smart sensors capable of collecting information about the environment and act on this based on the input data. With this scenario in mind, this project aims to analyze the efficiency and effectiveness of incremental classification methods, focusing on the Gaussian Mixture Model, for classification of data stream and, through the results, considering its application in intelligent sensors for insects classification.

Academic Publications

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

OLIVEIRA, Luan Soares. Non-stationary data streams classification with incremental algorithms based on Gaussian mixture models. 2015. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.