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Using anticipative hybrid extreme rotation forest to predict emergency service readmission risk
(Elsevier, 2017)
This paper provides a real life application of the recently published Anticipative Hybrid Extreme RotationForest (AHERF), which is an heterogeneous ensemble classifier that anticipates the correct fraction ofinstances from ...
Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
(Ieee-inst Electrical Electronics Engineers IncPiscatawayEUA, 2011)
Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation
(Elsevier B.V., 2017-10-01)
Background and objectives: The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist ...
A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach
(2011-11-28)
The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed ...
Evolutionary ensemble pattern classifiers
(2020-01-09)
RESUMEN:
Los comités de clasificadores son métodos que combinan las predicciones de diversos clasificadores base con el objetivo de producir un modelo con mejor poder predictivo; esto es, aplicar el concepto de que muchas ...
Methods for dynamic selection and fusion of ensemble of classifiers
(Universidade Federal de Pernambuco, 2014)
Optimum-path forest stacking-based ensemble for intrusion detection
(Springer, 2021-05-12)
Machine learning techniques have been extensively researched in the last years, mainly due to their effectiveness when dealing with recognition or classification applications. Typically, one can comprehend using a Machine ...
A differential evolution algorithm to optimise the combination of classifier and cluster ensembles
(Inderscience EnterprisesGeneva, 2015)
Unsupervised models can provide supplementary soft constraints to help classify new
data since similar instances are more likely to share the same class label. In this context, this paper reports on a study on how to make ...
Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
(IEEE, 2017-01)
Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional ...
Using metaheuristics to optimize the combination of classifier and cluster ensembles
(IOS PressAmsterdam, 2015)
We investigate how to make a simpler version of an existing algorithm, named 'C POT. 3'E, from Consensus between Classification and Clustering Ensembles, more user-friendly by automatically tuning its main parameters with ...