An investigation into the effects of label noise on Dynamic Selection algorithms
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In this article, the authors investigate the effects of label noise on a particular class of Ensemble Methods, that of Dynamic Selection algorithms, and they are especially interested in the behavior of the Fire-DES++ algorithm, a state-of-the-art algorithm which applies the Edited Nearest Neighbors (ENN) algorithm to deal with the effect of noise and imbalance.About:
This article is published in Information Fusion.The article was published on 2022-04-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Computer science & Noise (video).read more
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Journal ArticleDOI
Prediction of Aircraft Go-Around during Wind Shear Using the Dynamic Ensemble Selection Framework and Pilot Reports
TL;DR: In this article , the authors presented three Dynamic Ensemble Selection (DES) frameworks: Meta-Learning for Dynamic ensembles Selection (META-DES), Dynamic ensemble Selection Performance (DES-P), and K-Nearest Oracle Elimination (KNORAE), with homogeneous and heterogeneous pools of machine learning classifiers as base estimators for the prediction of aircraft go-around in wind shear (WS) events.
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