Ensemble Algorithms and Their Applications
Special Issue Information
Dear Colleagues,
We invite you to submit your latest research in the area of the development of ensemble algorithms to this Special Issue, “Ensemble Algorithms and Their Applications”. During the last decades, in the area of machine learning and data mining, the development of ensemble methods has gained a great attention from the scientific community. Machine learning ensemble methods combine multiple learning algorithms (classifiers) to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Combining multiple learning models has been theoretically and experimentally shown to provide significantly better performance than their single base learners. Thus, many ensemble learning algorithms have been proposed in the literature and found their application in various real word problems ranging from face and emotion recognition through text classification and medical diagnosis to financial forecasting.
The aim of this Special Issue is to present the recent advances related to all kinds of ensemble learning algorithms and methodologies and investigate the impact of their application in a diversity of real world problems.
Prof. Dr. Panagiotis E. Pintelas
Dr. Ioannis E. Livieris
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- Implementations of ensemble learning algorithms
- Theoretical framework for ensemble methods
- Ensemble learning methodologies dealing with imbalanced data
- Ensemble methods in clustering
- Subsampling and feature selection in multiple model machine learning
- Homogeneous and heterogeneous ensembles
- Black, white and gray box models
- Distributed ensemble learning algorithms
- Hybrid methods in prediction and classification
- Evolving, incremental and online ensemble learning
- Multi-objective ensemble learning
- Ensemble methods in agent and multi-agent systems
- Applications of ensemble methods in business, engineering, medicine, etc
- Comparisons among ensemble deep learners, single deep learners
- Deep ensemble for feature selection
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Published Papers
Algorithms - ISSN 1999-4893