160 citations · 164 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini +8
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performan…
An Eager Splitting Strategy for Online Decision Trees
Chaitanya Manapragada, Heitor M Gomes, Mahsa Salehi +2
Decision tree ensembles are widely used in practice. In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online t…