387 citations · 1.6k across the 48 of their papers we have counts for
21 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…
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira +3
Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implication…
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…
A writer-independent approach for offline signature verification using deep convolutional neural networks features
Victor L. F. Souza, Adriano L. I. Oliveira, Robert Sabourin
The use of features extracted using a deep convolutional neural network (CNN) combined with a writer-dependent (WD) SVM classifier resulted in significant improvement in performanc…
Dynamic voting in multi-view learning for radiomics applications
Hongliu Cao, Simon Bernard, Laurent Heutte +1
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radi…
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…
Segmentation-Free Approaches for Handwritten Numeral String Recognition
Andre G Hochuli, Luiz E S Oliveira, Alceu S Britto +1
This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length. A synthetic dataset of touching numeral strings of sizes 2-,…
Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Methods for learning feature representations for Offline Handwritten Signature Verification have been successfully proposed in recent literature, using Deep Convolutional Neural Ne…
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…
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
Hongliu Cao, Simon Bernard, Laurent Heutte +1
Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology I…
Dissimilarity-based representation for radiomics applications
Hongliu Cao, Simon Bernard, Laurent Heutte +1
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognosti…
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…
Deep Learning Architectures for Face Recognition in Video Surveillance
Saman Bashbaghi, Eric Granger, Robert Sabourin +1
Face recognition (FR) systems for video surveillance (VS) applications attempt to accurately detect the presence of target individuals over a distributed network of cameras. In vid…
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…