374 citations · 982 across the 22 of their papers we have counts for
24 papers
Perceptual Influence: Improving the Perceptual Loss Design for Low-Dose CT Enhancement
Gabriel A. Viana, Luis F. Alves Pereira, Tsang Ing Ren +2
Perceptual losses have emerged as powerful tools for training networks to enhance Low-Dose Computed Tomography (LDCT) images, offering an alternative to traditional pixel-wise loss…
A post-selection algorithm for improving dynamic ensemble selection methods
Paulo R. G. Cordeiro, George D. C. Cavalcanti, Rafael M. O. Cruz
Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS) approach that aims to select an ensemble for each query sample during the selection phase. Even with the pro…
The choice of scaling technique matters for classification performance
Lucas B. V. de Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz
Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all v…
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…
Selecting and combining complementary feature representations and classifiers for hate speech detection
Rafael M. O. Cruz, Woshington V. de Sousa, George D. C. Cavalcanti
Hate speech is a major issue in social networks due to the high volume of data generated daily. Recent works demonstrate the usefulness of machine learning (ML) in dealing with the…
Label noise detection under the Noise at Random model with ensemble filters
Kecia G. Moura, Ricardo B. C. Prudêncio, George D. C. Cavalcanti
Label noise detection has been widely studied in Machine Learning because of its importance in improving training data quality. Satisfactory noise detection has been achieved by ad…
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images
Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini +4
COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation…
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…
Evaluating Competence Measures for Dynamic Regressor Selection
Thiago J. M. Moura, George D. C. Cavalcanti, Luiz S. Oliveira
Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to estimate the target value of a given test pattern. This competence is…
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…
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…