Special Issue "Ensemble Algorithms and Their Applications"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 15 January 2020.

Special Issue Editors

Prof. Dr. Panagiotis E. Pintelas
E-Mail Website
Guest Editor
Department of Mathematics, University of Patras, GR 265-00 Patras, Greece
Interests: software engineering; agents and agent architectures; AI in education; intelligent tutoring systems; decision support systems; machine learning; data mining
Dr. Ioannis E. Livieris
E-Mail Website
Co-Guest Editor
Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, GR 263-34 Antirion, Greece
Interests: machine learning; data mining and its applications; decision support systems; neural networks algorithms; optimization algorithms
Special Issues and Collections in MDPI journals

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 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.

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

Published Papers (2 papers)

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Research

Open AccessArticle
Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece
Algorithms 2019, 12(11), 235; https://doi.org/10.3390/a12110235 - 06 Nov 2019
Abstract
This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future [...] Read more.
This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series. Full article
(This article belongs to the Special Issue Ensemble Algorithms and Their Applications)
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Open AccessArticle
A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays
Algorithms 2019, 12(3), 64; https://doi.org/10.3390/a12030064 - 16 Mar 2019
Cited by 3
Abstract
During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large [...] Read more.
During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large repositories of classified (labeled) images and mostly of unclassified (unlabeled) images from human experts. Machine learning methods such as semi-supervised learning algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, by exploiting the explicit classification information of labeled data with the information hidden in the unlabeled data. In the present work, we propose a new ensemble semi-supervised learning algorithm for the classification of lung abnormalities from chest X-rays based on a new weighted voting scheme. The proposed algorithm assigns a vector of weights on each component classifier of the ensemble based on its accuracy on each class. Our numerical experiments illustrate the efficiency of the proposed ensemble methodology against other state-of-the-art classification methods. Full article
(This article belongs to the Special Issue Ensemble Algorithms and Their Applications)
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Planned Papers

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.

1. Title: Exploring Ensemble Methods with Fuzzy Cognitive Maps Learning in Solving Practical Classification Problems

Author: Vassilis C. Gerogiannis

 

2. Title: An Ensemble Co-training Scheme for Binary Classification Problems

Author: Karlos S., Kostopoulos G. and Kotsiantis S

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