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Mach. Learn. Knowl. Extr., Volume 1, Issue 4 (December 2019) – 4 articles

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Open AccessArticle
Effect of Data Representation for Time Series Classification—A Comparative Study and a New Proposal
Mach. Learn. Knowl. Extr. 2019, 1(4), 1100-1120; https://doi.org/10.3390/make1040062 - 06 Dec 2019
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Abstract
Time series classification (TSC) is becoming very important in the area of pattern recognition with the increased availability of time series data in various natural and real life phenomena. TSC is a challenging problem because, due to the attributes being ordered, traditional machine [...] Read more.
Time series classification (TSC) is becoming very important in the area of pattern recognition with the increased availability of time series data in various natural and real life phenomena. TSC is a challenging problem because, due to the attributes being ordered, traditional machine learning algorithms for static data are not quite suitable for processing temporal data. Due to the gradual increase of computing power, a large number of TSC algorithms have been developed recently. In addition to traditional feature-based, model-based or distance-based algorithms, ensemble and deep networks have recently become popular for time series classification. Time series are essentially huge, and classifying raw data is computationally expensive in terms of both processing and storage. Representation techniques for data reduction and ease of visualization are needed for accurate classification. In this work a recurrence plot-based data representation is proposed and time series classification in conjunction with a deep neural network-based classifier has been studied. A simulation experiment with 85 benchmark data sets from UCR repository has been undertaken with several state of the art algorithms for time series classification in addition to our proposed scheme of classification for comparative study. It was found that, among non-ensemble algorithms, the proposed algorithm produces the highest classification accuracy for most of the data sets. Full article
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Open AccessArticle
Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis
Mach. Learn. Knowl. Extr. 2019, 1(4), 1084-1099; https://doi.org/10.3390/make1040061 - 05 Nov 2019
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Abstract
In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multiple gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance-partial least squares discriminant [...] Read more.
In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multiple gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance-partial least squares discriminant analysis when the signal-to-noise ratio and training sample size are sufficient. Full article
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Open AccessArticle
Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines Using the Theory of Functional Connections
Mach. Learn. Knowl. Extr. 2019, 1(4), 1058-1083; https://doi.org/10.3390/make1040060 - 09 Oct 2019
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Abstract
Differential equations (DEs) are used as numerical models to describe physical phenomena throughout the field of engineering and science, including heat and fluid flow, structural bending, and systems dynamics. While there are many other techniques for finding approximate solutions to these equations, this [...] Read more.
Differential equations (DEs) are used as numerical models to describe physical phenomena throughout the field of engineering and science, including heat and fluid flow, structural bending, and systems dynamics. While there are many other techniques for finding approximate solutions to these equations, this paper looks to compare the application of the Theory of Functional Connections (TFC) with one based on least-squares support vector machines (LS-SVM). The TFC method uses a constrained expression, an expression that always satisfies the DE constraints, which transforms the process of solving a DE into solving an unconstrained optimization problem that is ultimately solved via least-squares (LS). In addition to individual analysis, the two methods are merged into a new methodology, called constrained SVMs (CSVM), by incorporating the LS-SVM method into the TFC framework to solve unconstrained problems. Numerical tests are conducted on four sample problems: One first order linear ordinary differential equation (ODE), one first order nonlinear ODE, one second order linear ODE, and one two-dimensional linear partial differential equation (PDE). Using the LS-SVM method as a benchmark, a speed comparison is made for all the problems by timing the training period, and an accuracy comparison is made using the maximum error and mean squared error on the training and test sets. In general, TFC is shown to be slightly faster (by an order of magnitude or less) and more accurate (by multiple orders of magnitude) than the LS-SVM and CSVM approaches. Full article
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Open AccessArticle
Towards Image Classification with Machine Learning Methodologies for Smartphones
Mach. Learn. Knowl. Extr. 2019, 1(4), 1039-1057; https://doi.org/10.3390/make1040059 - 04 Oct 2019
Viewed by 626
Abstract
Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers [...] Read more.
Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery. Full article
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