Special Issue "New Trends in Computational Intelligence and Applications"

A special issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editors

Dr. Héctor-Gabriel Acosta-Mesa
Website
Guest Editor
Centro de Investigación en Inteligencia Artificial, University of Veracruz, 91000 Xalapa, Mexico
Interests: machine learning; medical image processing; unsupervised learning; artificial intelligence applications
Dr. Efrén Mezura-Montes
Website
Guest Editor
Centro de Investigación en Inteligencia Artificial, University of Veracruz, 91000 Xalapa, Mexico
Interests: evolutionary computing; swarm intelligence; complex optimization problem solving; machine learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will mainly consist of selected papers presented at the 1st Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2019, see https://bi-level.org/ciapp/ for detailed information). Papers considered to fit the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge.

The main topics of this Special Issue are:

  • Machine learning
  • Data mining
  • Statistical learning
  • Automatic image processing
  • Intelligent agents / multi-agent systems
  • Evolutionary computing
  • Swarm intelligence
  • Combinatorial and numerical optimization
  • Parallel and distributed computing in computational intelligence

Dr. Héctor-Gabriel Acosta-Mesa
Dr. Efrén Mezura-Montes
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. Mathematical and Computational Applications is an international peer-reviewed open access quarterly 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.

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle
Windowing as a Sub-Sampling Method for Distributed Data Mining
Math. Comput. Appl. 2020, 25(3), 39; https://doi.org/10.3390/mca25030039 - 30 Jun 2020
Abstract
Windowing is a sub-sampling method, originally proposed to cope with large datasets when inducing decision trees with the ID3 and C4.5 algorithms. The method exhibits a strong negative correlation between the accuracy of the learned models and the number of examples used to [...] Read more.
Windowing is a sub-sampling method, originally proposed to cope with large datasets when inducing decision trees with the ID3 and C4.5 algorithms. The method exhibits a strong negative correlation between the accuracy of the learned models and the number of examples used to induce them, i.e., the higher the accuracy of the obtained model, the fewer examples used to induce it. This paper contributes to a better understanding of this behavior in order to promote windowing as a sub-sampling method for Distributed Data Mining. For this, the generalization of the behavior of windowing beyond decision trees is established, by corroborating the observed negative correlation when adopting inductive algorithms of different nature. Then, focusing on decision trees, the windows (samples) and the obtained models are analyzed in terms of Minimum Description Length (MDL), Area Under the ROC Curve (AUC), Kulllback–Leibler divergence, and the similitude metric Sim1; and compared to those obtained when using traditional methods: random, balanced, and stratified samplings. It is shown that the aggressive sampling performed by windowing, up to 3% of the original dataset, induces models that are significantly more accurate than those obtained from the traditional sampling methods, among which only the balanced sampling is comparable in terms of AUC. Although the considered informational properties did not correlate with the obtained accuracy, they provide clues about the behavior of windowing and suggest further experiments to enhance such understanding and the performance of the method, i.e., studying the evolution of the windows over time. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications)
Open AccessFeature PaperArticle
Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition
Math. Comput. Appl. 2020, 25(2), 37; https://doi.org/10.3390/mca25020037 - 20 Jun 2020
Abstract
We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Nondominated Sorting Genetic Algorithm for [...] Read more.
We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Nondominated Sorting Genetic Algorithm for learning Bayesian networks (NS2BN) which is based on the well-known NSGA-II algorithm. The core idea is to reduce the implicit selection bias-variance decomposition while identifying a set of competitive models using both objectives. Numerical results suggest that, in stark contrast to the single-objective approach, our bi-objective approach is useful to find competitive Bayesian networks especially in the complexity. Furthermore, our approach presents the end user with a set of solutions by showing different Bayesian network and their respective MDL and classification accuracy results. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications)
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Open AccessArticle
Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison
Math. Comput. Appl. 2020, 25(2), 32; https://doi.org/10.3390/mca25020032 - 16 Jun 2020
Abstract
This work presents the assessment of the well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and one of its variants to optimize a proposed electric power production system. Such variant implements a chaotic model to generate the initial population, aiming to get a better [...] Read more.
This work presents the assessment of the well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and one of its variants to optimize a proposed electric power production system. Such variant implements a chaotic model to generate the initial population, aiming to get a better distributed Pareto front. The considered power system is composed of solar, wind and natural gas power sources, being the first two renewable energies. Three conflicting objectives are considered in the problem: (1) power production, (2) production costs and (3) CO2 emissions. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is also adopted in the comparison so as to enrich the empirical evidence by contrasting the NSGA-II versions against a non-Pareto-based approach. Spacing and Hypervolume are the chosen metrics to compare the performance of the algorithms under study. The obtained results suggest that there is no significant improvement by using the variant of the NSGA-II over the original version. Nonetheless, meaningful performance differences have been found between MOEA/D and the other two algorithms. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications)
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