Ensemble Algorithms and Their Applications

doi:10.3390/books978-3-03936-959-1

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Ensemble Algorithms and Their Applications

Edited by

September 2020

182 pages

ISBN978-3-03936-958-4 (Hardback)
ISBN978-3-03936-959-1 (PDF)

https://doi.org/10.3390/books978-3-03936-959-1

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This book is a reprint of the Special Issue Ensemble Algorithms and Their Applications that was published in

Computer Science & Mathematics

Summary

In recent decades, the development of ensemble learning methodologies has gained a significant attention from the scientific and industrial community, and found their application in various real-word problems. Theoretical and experimental evidence proved that ensemble models provide a considerably better prediction performance than single models. The main aim of this collection is to present the recent advances related to ensemble learning algorithms and investigate the impact of their application in a diversity of real-world problems. All papers possess significant elements of novelty and introduce interesting ensemble-based approaches, which provide readers with a glimpse of the state-of-the-art research in the domain.

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Hardback

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Keywords

machine learning; semi-supervised learning; self-labeled algorithms; classifiers; ensemble learning; weighted voting; image classification; lung abnormalities; fuzzy cognitive maps; neural networks; time series forecasting; ensemble learning; prediction; machine learning; natural gas; explainable machine learning; interpretable machine learning; semi-supervised learning; self-training algorithms; ensemble learning; black, white and grey box models; binary classification; co-training; ensemble methods; feature views; dynamic ensemble selection; Soft-Voting; model-agnostic meta-learning; ensemble learning; GIS; hyperspectral images; deep learning; remote sensing; scene classification; geospatial data; Zero-shot Learning; emotion recognition; ensemble algorithm; feature extraction; hybrid feature; machine learning; supervised learning; ensemble learning; sentiment analysis; multilabel classification; deep neural networks; pure emotion; Semeval 2018 Task 1; toxic comment classification; deep learning; ensemble learning; convolutional networks; long short-term memory; cryptocurrency; time-series; ensemble learning; homogeneous and heterogeneous ensembles; fusion strategies; voting schemes; model combination; black, white and gray box models; incremental and evolving learning