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Mathematics
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Advances of Machine Learning and Their Applications
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Advances of Machine Learning and Their Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 13434

Special Issue Editors

Prof. Dr. Ana Belén Nieto-Librero

E-Mail Website
Guest Editor
1. Department of Statistics, Universidad de Salamanca, 37008 Salamanca, Spain
2. Institute of Biomedical Research of Salamanca, 37008 Salamanca, Spain
Interests: multivariate analysis; big data; optimization methods; classification techniques; biplot
Prof. Dr. Nerea González-García

E-Mail Website
Guest Editor
1. Department of Statistics, Universidad de Salamanca, 37008 Salamanca, Spain
2. Institute of Biomedical Research of Salamanca, 37008 Salamanca, Spain
Interests: multivariate analysis; big data analysis; sparse modelling; three way data; biplot

Special Issue Information

Dear Colleagues,

Recently, there has been an increase in the use of statistical models to understand the phenomena that occur in different areas of knowledge. The reality we face is a multifactorial phenomenon in which its components are intrinsically interrelated and therefore, the approach to its analysis involves the use of multivariate techniques, including Machine Learning methods. In this sense, this Special Issue focuses on new proposals for data analysis or the use of existing techniques to provide solutions to problems encountered in different fields of science and industry. We invite researchers to publish and disseminate their recent achievements in this field through high quality articles on the pioneering use of multivariate data analysis methods. Proposals for new developments are also welcome.

Prof. Dr. Ana Belén Nieto-Librero
Prof. Dr. Nerea González-García
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 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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • Machine Learning
  • Multivariate Analysis
  • Big Data
  • Applied Statistics
  • Algorithm
  • Optimization Methods
  • Classification

Published Papers (6 papers)

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Research

19 pages, 1939 KiB  
Article
A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures
by Claudia C. Tusell-Rey, Oscar Camacho-Nieto, Cornelio Yáñez-Márquez, Yenny Villuendas-Rey, Ricardo Tejeida-Padilla and Carmen F. Rey Benguría
Mathematics 2022, 10(15), 2740; https://0-doi-org.brum.beds.ac.uk/10.3390/math10152740 - 02 Aug 2022
Cited by 2 | Viewed by 1265
Abstract
In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic [...] Read more.
In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic has attracted the attention of international research groups because a very promising vein of research has emerged: the application of some measures of data complexity in the pattern classification algorithms. This paper aims to analyze the response of the Customized Naïve Associative Classifier (CNAC) in data taken from the business area when some measures of data complexity are introduced. To perform this analysis, we used classification datasets from real-world related to business, 22 in total; then, we computed the value of nine measures of data complexity to compare the performance of the CNAC against other algorithms of the state of the art. A very important aspect of performing this task is the creation of an artificial dataset for meta-learning purposes, in which we considered the performance of CNAC, and then we trained a decision tree as meta learner. As shown, the CNAC classifier obtained the best results for 10 out of 22 datasets of the experimental study. Full article
(This article belongs to the Special Issue Advances of Machine Learning and Their Applications)
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16 pages, 3173 KiB  
Article
Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
by Irene Mariñas-Collado, Ana E. Sipols, M. Teresa Santos-Martín and Elisa Frutos-Bernal
Mathematics 2022, 10(15), 2670; https://0-doi-org.brum.beds.ac.uk/10.3390/math10152670 - 28 Jul 2022
Cited by 3 | Viewed by 1559
Abstract
The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number [...] Read more.
The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number of stops in the bus network, these are divided into clusters and then different models are fitted for a representative of each of the clusters. The aim is to compare and combine the predictions associated with traditional methods, such as exponential smoothing or ARIMA, with machine learning methods, such as support vector machines or artificial neural networks. Moreover, support vector machine predictions are improved by incorporating explanatory variables with temporal structure and moving averages. Finally, through cointegration techniques, the results obtained for the representative of each group are extrapolated to the rest of the series within the same cluster. A case study in the city of Salamanca (Spain) is presented to illustrate the problem. Full article
(This article belongs to the Special Issue Advances of Machine Learning and Their Applications)
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26 pages, 4747 KiB  
Article
Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem
by Salam Salameh Shreem, Mohd Zakree Ahmad Nazri, Salwani Abdullah and Nor Samsiah Sani
Mathematics 2022, 10(3), 374; https://0-doi-org.brum.beds.ac.uk/10.3390/math10030374 - 26 Jan 2022
Cited by 8 | Viewed by 1818
Abstract
Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious [...] Read more.
Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy. Full article
(This article belongs to the Special Issue Advances of Machine Learning and Their Applications)
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18 pages, 3148 KiB  
Article
Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments
by Wing Son Loh, Ren Jie Chin, Lloyd Ling, Sai Hin Lai and Eugene Zhen Xiang Soo
Mathematics 2021, 9(23), 3141; https://0-doi-org.brum.beds.ac.uk/10.3390/math9233141 - 06 Dec 2021
Cited by 5 | Viewed by 2451
Abstract
Sedimentation management is one of the primary factors in achieving sustainable development of water resources. However, due to difficulties in conducting in-situ tests, and the complex nature of fine sediments, it remains a challenging task when dealing with issues related to settling velocity. [...] Read more.
Sedimentation management is one of the primary factors in achieving sustainable development of water resources. However, due to difficulties in conducting in-situ tests, and the complex nature of fine sediments, it remains a challenging task when dealing with issues related to settling velocity. Hence, the machine learning model appears as a suitable tool to predict the settling velocity of fine sediments in water bodies. In this study, three different machine learning-based models, namely, the radial basis function neural network (RBFNN), back propagation neural network (BPNN), and self-organizing feature map (SOFM), were developed with four hydraulic parameters, including the inlet depth, particle size, and the relative x and y particle positions. The five distinct statistical measures, consisting of the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), mean value accounted for (MVAF), and total variance explained (TVE), were used to assess the performance of the models. The SOFM with the 25 × 25 Kohonen map had shown superior results with RMSE of 0.001307, NSE of 0.7170, MAE of 0.000647, MVAF of 101.25%, and TVE of 71.71%. Full article
(This article belongs to the Special Issue Advances of Machine Learning and Their Applications)
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14 pages, 1873 KiB  
Article
Proposal of the Dichotomous STATIS DUAL Method: Software and Application for the Analysis of Dichotomous Data, Applied to the Test of Learning Styles in University Students
by Victoria I. Ballesteros-Espinoza, Miguel Rodríguez-Rosa, Ana B. Sánchez-García and Purificación Vicente-Galindo
Mathematics 2021, 9(21), 2797; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212797 - 04 Nov 2021
Cited by 1 | Viewed by 1539
Abstract
The present work analyzed a review of methods for analyzing sequences of matrices or dichotomous data. A new method for a sequence of dichotomous matrices with a different number of rows is presented; the Dichotomous STATIS DUAL. Suppose we match the sequence of [...] Read more.
The present work analyzed a review of methods for analyzing sequences of matrices or dichotomous data. A new method for a sequence of dichotomous matrices with a different number of rows is presented; the Dichotomous STATIS DUAL. Suppose we match the sequence of matrices by different years, with this method. In that case, we can graphically represent the relations among the different columns of all the matrices, and the relations between those columns and the different years, because everything can be represented in the same plots. As in all STATIS methods, three different plots can get: (i) the interstructure, with the relations among the years; (ii) the compromise, with the stable part of the relations between the columns; and (iii) the intrastructure (also known as trajectories), with the relations between columns and years, in other words, the evolution of the columns through the time. This new mathematical method can be used with all kinds of dichotomous data, thanks to the software we propose. In the present work, the software was applied to the assessment of learning styles. Full article
(This article belongs to the Special Issue Advances of Machine Learning and Their Applications)
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21 pages, 5192 KiB  
Article
Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model
by Fei Teng, Yafei Song and Xinpeng Guo
Mathematics 2021, 9(19), 2412; https://0-doi-org.brum.beds.ac.uk/10.3390/math9192412 - 28 Sep 2021
Cited by 19 | Viewed by 3310
Abstract
The prerequisite for victory in war is the rapid and accurate identification of the tactical intention of the target on the battlefield. The efficiency of manual recognition of the combat intention of air targets is becoming less and less effective with the advent [...] Read more.
The prerequisite for victory in war is the rapid and accurate identification of the tactical intention of the target on the battlefield. The efficiency of manual recognition of the combat intention of air targets is becoming less and less effective with the advent of information warfare. Moreover, if the traditional method of combat intention of air targets is based only on data from a single moment in time, the characteristic information on the time-series data is difficult to capture effectively. In this context, we design a new deep learning method attention mechanism with temporal convolutional network and bidirectional gated recurrent unit (Attention-TCN-BiGRU) to improve the recognition of the combat intent of air targets. Specifically, suitable characteristics are selected based on the combat mission and air posture to construct a characteristic set of air target intentions and encode them into temporal characteristics. Each characteristic in the characteristic set is given an appropriate weight through the attention mechanism. In addition, temporal convolutional network (TCN) is used to mine the data for latent characteristics and bidirectional gated recurrent unit (BiGRU) is used to capture long-term dependencies in the data. Experiments comparing with other methods and ablation demonstrate that Attention-TCN-BiGRU outperforms state-of-the-art methods in terms of accuracy in recognizing target intent in the air. Full article
(This article belongs to the Special Issue Advances of Machine Learning and Their Applications)
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