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Found 23 papers, 8 papers with code
A post-selection algorithm for improving dynamic ensemble selection methods
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.
The choice of scaling technique matters for classification performance
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.
Local overlap reduction procedure for dynamic ensemble selection
Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class.
Selecting and combining complementary feature representations and classifiers for hate speech detection
This work argues that a combination of multiple feature extraction techniques and different classification models is needed.
Label noise detection under the Noise at Random model with ensemble filters
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.
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images
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
Multi-label learning for dynamic model type recommendation
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.
Evaluating Competence Measures for Dynamic Regressor Selection
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.
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
META-DES.Oracle: Meta-learning and feature selection for ensemble selection
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence.
Analyzing different prototype selection techniques for dynamic classifier and ensemble selection
The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample.
A Method For Dynamic Ensemble Selection Based on a Filter and an Adaptive Distance to Improve the Quality of the Regions of Competence
Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern.
On Meta-Learning for Dynamic Ensemble Selection
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.
META-DES.H: a dynamic ensemble selection technique using meta-learning and a dynamic weighting approach
In this paper, we propose improvements to the training and generalization phase of the META-DES framework.
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
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.
META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning
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.
Online local pool generation for dynamic classifier selection: an extended version
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.
Dynamic Ensemble Selection VS K-NN: why and when Dynamic Selection obtains higher classification performance?
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.
An Ensemble Generation Method Based on Instance Hardness
Techniques such as Bagging and Boosting have been successfully applied to a variety of problems.
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
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.
On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems.
DESlib: A Dynamic ensemble selection library in Python
DESlib is an open-source python library providing the implementation of several dynamic selection techniques.
A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers
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.
