Providing theoretical guarantees to the detection of concept drift in data streams

• Grant number: 17/16548-6
• Support Opportunities: Scholarships abroad - Research
• Start date: August 01, 2018
• End date: January 31, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Rodrigo Fernandes de Mello
• Grantee: Rodrigo Fernandes de Mello
• Host Investigator: Albert Bifet
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: ParisTech, France
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

With the objective of modeling data stream changes, several researchers have been designing new approaches to detect concept drifts. A concept is characterized by a sequence of observations produced by a same generating process. Researchers are interested in detecting concept drift in order to support specialists to make decisions on the phenomena that generated such streams. Currently, there are two main research areas devoted to the concept drift detection: the first is based on Supervised learning, while the second relies on unsupervised approaches. Both lack in terms of providing theoretical guarantees while detecting drifts, once the first relaxes the assumption of data independency, required by the Empirical Risk Minimization Principle defined in the context of the Statistical Learning Theory, and the second fails due to no theoretical framework ensures learning, therefore detections are usually caused by the algorithm parametrization and not due to data changes. In order to tackle such drawbacks, this research project aims at formulating a theoretical framework to ensure that detections of concept drift in data streams are due to modifications occurred in in the observations collected along time and not by chance nor parametrization. Furthermore, we will design and implement an algorithm to detect concept drift under such theoretical guarantees. Experiments will be performed using transitions among data stream concepts produced by different synthetic generating processes, as well as by real-world streams.

Scientific publications

(7)

(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)

VAZ, YULE; DE MELLO, RODRIGO FERNANDES; GROSSI FERREIRA, CARLOS HENRIQUE. Coarse-refinement dilemma: On generalization bounds for data clustering. EXPERT SYSTEMS WITH APPLICATIONS, v. 184, DEC 1 2021. (17/16548-6)

DE MELLO, RODRIGO E.; MANAPRAGADA, CHAITANYA; BIFET, ALBERT. Measuring the Shattering coefficient of Decision Tree models. EXPERT SYSTEMS WITH APPLICATIONS, v. 137, p. 443-452, DEC 15 2019. (17/16548-6)

DE MELLO, RODRIGO F.; RIOS, RICARDO A.; PAGLIOSA, PAULO A.; LOPES, CAIO S.. Concept drift detection on social network data using cross-recurrence quantification analysis. Chaos, v. 28, n. 8, AUG 2018. (17/16548-6, 13/07375-0)

DE MELLO, RODRIGO F.; VAZ, YULE; GROSSI, CARLOS H.; BIFET, ALBERT. On learning guarantees to unsupervised concept drift detection on data streams. EXPERT SYSTEMS WITH APPLICATIONS, v. 117, p. 90-102, MAR 1 2019. (17/16548-6)

DUARTE, FELIPE S. L. G.; RIOS, RICARDO A.; HRUSCHKA, EDUARDO R.; DE MELLO, RODRIGO F.. Decomposing time series into deterministic and stochastic influences: A survey. DIGITAL SIGNAL PROCESSING, v. 95, DEC 2019. (17/16548-6, 13/07375-0, 14/21636-3)

NAZARE, TIAGO S.; PARANHOS DA COSTA, GABRIEL B.; DE MELLO, RODRIGO F.; PONTI, MOACIR A.; IEEE. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 7-pg., 2018-01-01. (15/05310-3, 17/16548-6, 13/07375-0, 15/04883-0, 16/16111-4)

DUARTE, FELIPE S. L. G.; RIOS, RICARDO A.; HRUSCHKA, EDUARDO R.; DE MELLO, RODRIGO F.; IEEE. Time Series Decomposition Using Spring System Applied on Phase Spaces. 2018 7TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), v. N/A, p. 6-pg., 2018-01-01. (13/07375-0, 14/21636-3, 17/16548-6)