6 citations · 7 across the 5 of their papers we have counts for
5 papers
Concept Drift from a Causal Perspective
Eduardo V. L. Barboza, Jean Paul Barddal, Robert Sabourin +1
Concept drift is a common phenomenon in real-world data streams, in which changes in the data-generating distribution can degrade predictive model performance. Most existing defini…
Online K-d tree for approximate neighborhood search in data streams
Eduardo V. L. Barboza, Robert Sabourin, Rafael M. O. Cruz
The k-Nearest Neighbors (kNN) algorithm has long been widely used in Machine Learning (ML) applications. However, the main concern when using it is the computational cost required…
CaDrift: A Time-dependent Causal Generator of Drifting Data Streams
Eduardo V. L. Barboza, Jean Paul Barddal, Robert Sabourin +1
This work presents Causal Drift Generator (CaDrift), a time-dependent synthetic data generator framework based on Structural Causal Models (SCMs). The framework produces a virtuall…
IncA-DES: An incremental and adaptive dynamic ensemble selection approach using online K-d tree neighborhood search for data streams with concept drift
Eduardo V. L. Barboza, Paulo R. Lisboa de Almeida, Alceu de Souza Britto +2
Data streams pose challenges not usually encountered in batch-based ML. One of them is concept drift, which is characterized by the change in data distribution over time. Among man…
Distance Functions and Normalization Under Stream Scenarios
Eduardo V. L. Barboza, Paulo R. Lisboa de Almeida, Alceu de Souza Britto +1
Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not kn…