Time series classification by similarity and extraction of intrinsic attributes with application to insect species identification

• Grant number: 11/04054-2
• Support Opportunities: Scholarships in Brazil - Master
• Start date: March 01, 2012
• End date: February 28, 2014
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Gustavo Enrique de Almeida Prado Alves Batista
• Grantee: Diego Furtado Silva
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 12/18985-0 - Insect species identification by means of optical sensor using music and speech signals classification techniques, BE.EP.MS

Abstract

Integrating sequential and temporal data into the Data Mining process is of one of the most important challenges in Machine Learning, for tasks such as clustering, classification and prediction. In the case of classification, there exist two approaches frequently used with temporal data: similarity and feature extraction. Classification by similarity uses a distance function to identify the most similar time series to a query series, and the query class is assigned to the dominant class among the similar time series; the classification by feature extraction performs a search for local features and uses those features to induce a classifier. In this project we are interested in classifying signals from disease vectors mosquitoes using time series data obtained from an optical sensor. Our objective is to compare classification methods by similarity and feature extraction applied to this context. Our previous experience indicates that the feature extraction approach has shown the most promising results. Given the large volume of insect data collected so far, our main object is to research, evaluate and compare automatic and non-supervised methods for intrinsic feature identification. Some of these methods have been widely used by the signal processing community to classify several types of sound signals, such as musical notes.

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)

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. (11/04054-2, 12/07295-3, 11/17698-5, 12/50714-7)

BATISTA, GUSTAVO; SILVA, DIEGO; PRATI, RONALDO; WANI, MA; KHOSHGOFTAAR, T; ZHU, X; SELIYA, N. An Experimental Design to Evaluate Class Imbalance Treatment Methods. 2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 2, v. N/A, p. 7-pg., 2012-01-01. (12/07295-3, 11/04054-2)

Academic Publications

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

SILVA, Diego Furtado. Classification of time series similarity and feature extraction with application to automatic identification of insects. 2014. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.