1 code implementation • 25 Sep 2023 • Paulo R. G. Cordeiro, George D. C. Cavalcanti, Rafael M. O. Cruz
To evaluate this idea, we introduce the Post-Selection Dynamic Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates ensembles selected by several DES techniques using different metrics.
1 code implementation • 23 Dec 2022 • Lucas B. V. de Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz
In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios.
1 code implementation • 16 Jun 2022 • Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class.
1 code implementation • 18 Jan 2022 • Rafael M. O. Cruz, Woshington V. de Sousa, George D. C. Cavalcanti
This work argues that a combination of multiple feature extraction techniques and different classification models is needed.
1 code implementation • 2 Dec 2021 • Kecia G. Moura, Ricardo B. C. Prudêncio, George D. C. Cavalcanti
This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent.
1 code implementation • 21 Sep 2020 • Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini, Luiz S. Oliveira, Loris Nanni, George D. C. Cavalcanti, Yandre M. G. Costa
We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another.
Computed Tomography (CT) Explainable artificial intelligence +2
1 code implementation • 1 Apr 2020 • Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant model types based on the local data complexity of the region surrounding each test sample.
no code implementations • 9 Apr 2019 • Thiago J. M. Moura, George D. C. Cavalcanti, Luiz S. Oliveira
Three DRS systems were compared against individual regressor and static systems that use the Mean and the Median to combine the outputs of the regressors from the ensemble.
no code implementations • 22 Nov 2018 • Rafael M. O. Cruz, Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
no code implementations • 1 Nov 2018 • 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.
no code implementations • 1 Nov 2018 • Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample.
no code implementations • 1 Nov 2018 • 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.
no code implementations • 1 Nov 2018 • Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
The meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance.
no code implementations • 1 Nov 2018 • Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
In this paper, we propose improvements to the training and generalization phase of the META-DES framework.
no code implementations • 1 Oct 2018 • Rafael M. O. Cruz, Dayvid V. R. Oliveira, George D. C. Cavalcanti, Robert Sabourin
Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class.
no code implementations • 30 Sep 2018 • Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti, Tsang Ing Ren
The meta-features are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance.
no code implementations • 5 Sep 2018 • Mariana A. Souza, George D. C. Cavalcanti, Rafael M. O. Cruz, Robert Sabourin
Thus, we propose in this work an online pool generation method that produces a locally accurate pool for test samples in difficult regions of the feature space.
no code implementations • 21 Apr 2018 • Rafael M. O. Cruz, Hiba H. Zakane, Robert Sabourin, George D. C. Cavalcanti
Experiments are performed on 18 state-of-the-art DS techniques over 30 classification datasets and results show that DS methods present a significant boost in classification accuracy even though they use the same neighborhood as the K-NN.
no code implementations • 20 Apr 2018 • Felipe N. Walmsley, George D. C. Cavalcanti, Dayvid V. R. Oliveira, Rafael M. O. Cruz, Robert Sabourin
Techniques such as Bagging and Boosting have been successfully applied to a variety of problems.
no code implementations • 18 Apr 2018 • Dayvid V. R. Oliveira, George D. C. Cavalcanti, Thyago N. Porpino, Rafael M. O. Cruz, Robert Sabourin
The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats.
no code implementations • 11 Mar 2018 • Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
2 code implementations • 14 Feb 2018 • Rafael M. O. Cruz, Luiz G. Hafemann, Robert Sabourin, George D. C. Cavalcanti
DESlib is an open-source python library providing the implementation of several dynamic selection techniques.
no code implementations • 2 Sep 2015 • Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
In order to perform a more robust ensemble selection, we proposed the META-DES framework using meta-learning, where multiple criteria are encoded as meta-features and are passed down to a meta-classifier that is trained to estimate the competence level of a given classifier.