1 citations · 1 across the 1 of their papers we have counts for
17 papers
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…
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…