research
∙
12/23/2022
The choice of scaling technique matters for classification performance
Dataset scaling, also known as normalization, is an essential preprocess...
share
research
∙
06/16/2022
Local overlap reduction procedure for dynamic ensemble selection
Class imbalance is a characteristic known for making learning more chall...
research
∙
01/18/2022
Selecting and combining complementary feature representations and classifiers for hate speech detection
Hate speech is a major issue in social networks due to the high volume o...
research
∙
12/02/2021
Label noise detection under the Noise at Random model with ensemble filters
Label noise detection has been widely studied in Machine Learning becaus...
research
∙
04/01/2020
Multi-label learning for dynamic model type recommendation
Dynamic selection techniques aim at selecting the local experts around e...
research
∙
04/09/2019
Evaluating Competence Measures for Dynamic Regressor Selection
Dynamic regressor selection (DRS) systems work by selecting the most com...
research
∙
11/22/2018
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Class-imbalance refers to classification problems in which many more ins...
research
∙
11/01/2018
Analyzing different prototype selection techniques for dynamic classifier and ensemble selection
In dynamic selection (DS) techniques, only the most competent classifier...
research
∙
11/01/2018
META-DES.H: a dynamic ensemble selection technique using meta-learning and a dynamic weighting approach
In Dynamic Ensemble Selection (DES) techniques, only the most competent ...
research
∙
11/01/2018
On Meta-Learning for Dynamic Ensemble Selection
In this paper, we propose a novel dynamic ensemble selection framework u...
research
∙
11/01/2018
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 classifier...
research
∙
11/01/2018
META-DES.Oracle: Meta-learning and feature selection for ensemble selection
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable...
research
∙
10/01/2018
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
Despite being very effective in several classification tasks, Dynamic En...
research
∙
09/30/2018
META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning
Dynamic ensemble selection systems work by estimating the level of compe...
research
∙
09/05/2018
Online local pool generation for dynamic classifier selection: an extended version
Dynamic Classifier Selection (DCS) techniques have difficulty in selecti...
research
∙
04/21/2018
Dynamic Ensemble Selection VS K-NN: why and when Dynamic Selection obtains higher classification performance?
Multiple classifier systems focus on the combination of classifiers to o...
research
∙
04/20/2018
An Ensemble Generation Method Based on Instance Hardness
In Machine Learning, ensemble methods have been receiving a great deal o...
research
∙
04/20/2018
An Ensemble Generation MethodBased on Instance Hardness
In Machine Learning, ensemble methods have been receiving a great deal o...
research
∙
04/18/2018
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
Dynamic Ensemble Selection (DES) techniques aim to select locally compet...
research
∙
03/11/2018
On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Class-imbalance refers to classification problems in which many more ins...
research
∙
02/14/2018
DESlib: A Dynamic ensemble selection library in Python
DESlib is an open-source python library providing the implementation of ...
research
∙
09/02/2015
