160 citations · 227 across the 9 of their papers we have counts for
9 papers
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concep…
Fast & Furious: Modelling Malware Detection as Evolving Data Streams
Fabrício Ceschin, Marcus Botacin, Heitor Murilo Gomes +3
Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. T…
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing
Guilherme Cassales, Heitor Gomes, Albert Bifet +2
In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap…
Improving the performance of bagging ensembles for data streams through mini-batching
Guilherme Cassales, Heitor Gomes, Albert Bifet +2
Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and tra…
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Heitor Murilo Gomes, Maciej Grzenda, Rodrigo Mello +3
Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning p…
STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection
Vitor Cerqueira, Heitor Murilo Gomes, Albert Bifet +1
Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive m…