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10 papers · 2 filters
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…
A Method For Dynamic Ensemble Selection Based on a Filter and an Adaptive Distance to Improve the Quality of the Regions of Competence
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. This is done by defining a region around the query pat…
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…