387 citations · 1.6k across the 48 of their papers we have counts for
24 papers · 1 filter
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…
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…
Local overlap reduction procedure for dynamic ensemble selection
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti +1
Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class. A promising approach…
Dynamic Ensemble Selection Using Fuzzy Hyperboxes
Reza Davtalab, Rafael M. O. Cruz, Robert Sabourin
Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query samp…
Classifier Pool Generation based on a Two-level Diversity Approach
Marcos Monteiro, Alceu S. Britto, Jean P. Barddal +2
This paper describes a classifier pool generation method guided by the diversity estimated on the data complexity and classifier decisions. First, the behavior of complexity measur…
Random Forest for Dissimilarity-based Multi-view Learning
Simon Bernard, Hongliu Cao, Robert Sabourin +1
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies ar…
A Novel Random Forest Dissimilarity Measure for Multi-View Learning
Hongliu Cao, Simon Bernard, Robert Sabourin +1
Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between t…
Multi-label learning for dynamic model type recommendation
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti +1
Dynamic selection techniques aim at selecting the local experts around each test sample in particular for performing its classification. While generating the classifier on a local…
A Decision-Based Dynamic Ensemble Selection Method for Concept Drift
Regis Antonio Saraiva Albuquerque, Albert Franca Josua Costa, Eulanda Miranda dos Santos +2
We propose an online method for concept driftdetection based on dynamic classifier ensemble selection. Theproposed method generates a pool of ensembles by promotingdiversity among…
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Rafael M. O. Cruz, Mariana A. Souza, Robert Sabourin +1
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention…
On Meta-Learning for Dynamic Ensemble Selection
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is…
META-DES.H: a dynamic ensemble selection technique using meta-learning and a dynamic weighting approach
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate th…
Analyzing different prototype selection techniques for dynamic classifier and ensemble selection
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The m…
META-DES.Oracle: Meta-learning and feature selection for ensemble selection
Rafael M. O Cruz, Robert Sabourin, George D. C. Cavalcanti
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence. There are several criteria available to measure the…
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
Rafael M. O. Cruz, Dayvid V. R. Oliveira, George D. C. Cavalcanti +1
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competen…
META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti +1
Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a…
Online local pool generation for dynamic classifier selection: an extended version
Mariana A. Souza, George D. C. Cavalcanti, Rafael M. O. Cruz +1
Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely…
An Ensemble Generation Method Based on Instance Hardness
Felipe N. Walmsley, George D. C. Cavalcanti, Dayvid V. R. Oliveira +2
In Machine Learning, ensemble methods have been receiving a great deal of attention. Techniques such as Bagging and Boosting have been successfully applied to a variety of problems…
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
Dayvid V. R. Oliveira, George D. C. Cavalcanti, Thyago N. Porpino +2
Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence…
DESlib: A Dynamic ensemble selection library in Python
Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin +1
DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) \emph{dcs}, containing…
Bayesian Hyperparameter Optimization for Ensemble Learning
Julien-Charles Lévesque, Christian Gagné, Robert Sabourin
In this paper, we bridge the gap between hyperparameter optimization and ensemble learning by performing Bayesian optimization of an ensemble with regards to its hyperparameters. O…
A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
Dynamic ensemble selection (DES) techniques work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to c…
A Classifier-free Ensemble Selection Method based on Data Diversity in Random Subspaces
Albert H. R. Ko, Robert Sabourin, Alceu S. Britto +1
The Ensemble of Classifiers (EoC) has been shown to be effective in improving the performance of single classifiers by combining their outputs, and one of the most important proper…