374 citations · 982 across the 22 of their papers we have counts for
14 papers · 1 filter
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…
A Method For Dynamic Ensemble Selection Based on a Filter and an Adaptive Distance to Improve the Quality of the Regions of Competence
Rafael M. O. Cruz, George D. C. Cavalcanti, Tsang Ing Ren
Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pat…
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…
Dynamic Ensemble Selection VS K-NN: why and when Dynamic Selection obtains higher classification performance?
Rafael M. O. Cruz, Hiba H. Zakane, Robert Sabourin +1
Multiple classifier systems focus on the combination of classifiers to obtain better performance than a single robust one. These systems unfold three major phases: pool generation,…
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…
On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
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…
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…