Mathematical and Computational Applications

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

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Published Papers (3 papers)

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Research

20 pages, 426 KiB

Open AccessFeature PaperArticle

Windowing as a Sub-Sampling Method for Distributed Data Mining

by David Martínez-Galicia, Alejandro Guerra-Hernández, Nicandro Cruz-Ramírez, Xavier Limón and Francisco Grimaldo

Math. Comput. Appl. 2020, 25(3), 39; https://doi.org/10.3390/mca25030039 - 30 Jun 2020

Cited by 1 | Viewed by 2747

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 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)

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15 pages, 550 KiB

Open AccessFeature PaperArticle

Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

by Vicente-Josué Aguilera-Rueda, Nicandro Cruz-Ramírez and Efrén Mezura-Montes

Math. Comput. Appl. 2020, 25(2), 37; https://doi.org/10.3390/mca25020037 - 20 Jun 2020

Cited by 5 | Viewed by 2327

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 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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14 pages, 502 KiB

Open AccessArticle

Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison

by Gustavo-Adolfo Vargas-Hákim, Efrén Mezura-Montes and Edgar Galván

Math. Comput. Appl. 2020, 25(2), 32; https://doi.org/10.3390/mca25020032 - 16 Jun 2020

Cited by 5 | Viewed by 2653

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 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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