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Special Issue "Ensemble Algorithms and Their Applications"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: 15 January 2020.
Interests: software engineering; agents and agent architectures; AI in education; intelligent tutoring systems; decision support systems; machine learning; data mining
Interests: machine learning; data mining and its applications; decision support systems; neural networks algorithms; optimization algorithms
Special Issues and Collections in MDPI journals
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
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind 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 1000 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.
- 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
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Author: Vassilis C. Gerogiannis
2. Title: An Ensemble Co-training Scheme for Binary Classification Problems
Author: Karlos S., Kostopoulos G. and Kotsiantis S