Multi-target data stream mining

• Grant number: 18/07319-6
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2018
• End date: January 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: André Carlos Ponce de Leon Ferreira de Carvalho
• Grantee: Saulo Martiello Mastelini
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
• Associated scholarship(s): 21/10488-7 - Online nearest neighbor search, BE.EP.DR

Abstract

The amount of available data has increased in large amounts in past years. The development of new technologies, and fast and cheap ways of communicating have promoted this phenomena. Traditional machine learning approaches, i.e. batch learning, became inadequate to scenarios where potentially endless flows of information may exist. In this sense, new techniques were developed to deal with such problems treating the incoming information as data streams. However, the research in data streams is still a emerging field, constantly looking for solutions with compromise between low complexity and predictive efficiency. Therefore, only a few works encompassed more sophisticated predictive problems, such as the prediction of multiple structured outputs. Among these predictive problems, the multi-target regression field has been reported as an existing and open challenge in data streams. The few proposed methods for these tasks employed techniques which were derived from online classification problems, in their majority. Nonetheless, new solutions for classification were proposed in recent years, being them feasible for posterior adaptation. Besides, new solutions can be developed using as basis the knowledge from state-of-the-art batch multi-target regression techniques, as well as, the further exploration of the possibly existing inter-target correlations in such problems.

Scientific publications

(8)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

MASTELINI, SAULO MARTIELLO; CASSAR, DANIEL R.; ALCOBACA, EDESIO; BOTARI, TIAGO; DE CARVALHO, ANDRE C. P. L. F.; ZANOTTO, EDGAR D.. Machine learning unveils composition-property relationships in chalcogenide glasses. ACTA MATERIALIA, v. 240, p. 13-pg., 2022-09-09. (18/14819-5, 13/07793-6, 17/12491-0, 18/07319-6, 17/06161-7, 13/07375-0)

SANTANA, EVERTON JOSE; DOS SANTOS, FELIPE RODRIGUES; MASTELINI, SAULO MARTIELLO; MELQUIADES, FABIO LUIZ; BARBON JR, SYLVIO. Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, v. 209, FEB 15 2021. (18/07319-6)

ALCOBACA, EDESIO; MASTELINI, SAULO MARTIELLO; BOTARI, TIAGO; PIMENTEL, BRUNO ALMEIDA; CASSAR, DANIEL ROBERTO; DE LEON FERREIRA DE CARVALHO, ANDRE CARLOS PONCE; ZANOTTO, EDGAR DUTRA. Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures. ACTA MATERIALIA, v. 188, p. 92-100, APR 15 2020. (17/12491-0, 13/07375-0, 18/07319-6, 17/06161-7, 17/20265-0, 13/07793-6, 18/14819-5)

MASTELINI, SAULO MARTIELLO; CARVALHO, ANDRE CARLOS PONCE DE LEON FERREIRA DE. Using dynamical quantization to perform split attempts in online tree regressors. PATTERN RECOGNITION LETTERS, v. 145, p. 37-42, MAY 2021. (18/07319-6)

CASSAR, R. DANIEL; MASTELINI, SAULO MARTIELLO; BOTARI, TIAGO; ALCOBACA, EDESIO; DE CARVALHO, C. P. L. F. ANDRE; ZANOTTO, D. EDGAR. Predicting and interpreting oxide glass properties by machine learning using large datasets. CERAMICS INTERNATIONAL, v. 47, n. 17, p. 23958-23972, SEP 1 2021. (18/14819-5, 13/07375-0, 17/12491-0, 13/07793-6, 18/07319-6)

MASTELINI, SAULO MARTIELLO; MONTIEL, JACOB; GOMES, HEITOR MURILO; BIFET, ALBERT; PFAHRINGER, BERNHARD; DE CARVALHO, ANDRE C. P. L. F.; XUE, B; PECHENIZKIY, M; KOH, YS. Fast and lightweight binary and multi-branch Hoeffding Tree Regressors. 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS ICDMW 2021, v. N/A, p. 9-pg., 2021-01-01. (18/07319-6)

MASTELINI, SAULO MARTIELLO; SANTANA, EVERTON JOSE; CERRI, RICARDO; BARBON JR, SYLVIO. DSTARS: A multi-target deep structure for tracking asynchronous regressor stacking. APPLIED SOFT COMPUTING, v. 91, JUN 2020. (13/07375-0, 18/07319-6)

AGUIAR, GABRIEL JONAS; MANTOVANI, RAFAEL GOMES; MASTELINI, SAULO M.; DE CARVALHO, ANDRE C. P. F. L.; CAMPOS, GABRIEL F. C.; BARBON JUNIOR, SYLVIO. A meta-learning approach for selecting image segmentation algorithm. PATTERN RECOGNITION LETTERS, v. 128, p. 480-487, DEC 1 2019. (16/18615-0, 12/23114-9, 13/07375-0, 18/07319-6)

Academic Publications

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

MASTELINI, Saulo Martiello. Efficient online tree, rule-based and distance-based algorithms. 2023. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.