Combination of Feature Selection and Learning Methods for IoT Data Fusion
In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and...
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Amirkabir University of Technology
2017-12-01
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| Series: | AUT Journal of Electrical Engineering |
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| Online Access: | https://eej.aut.ac.ir/article_1960_5b7511e4f87d3b6a9eb1a6bc95cececc.pdf |
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| author | V. Sattari-Naeini Zahra Parizi-Nejad |
| author_facet | V. Sattari-Naeini Zahra Parizi-Nejad |
| author_sort | V. Sattari-Naeini |
| collection | DOAJ |
| description | In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessingthe data set based on curve fitting, reducing the data dimension and identifying the most effective featuresets according to data correlation, training classification algorithms, and finally predicting new databased on classification algorithms. The results derived from five compound schemes are investigated andcompared with each other with three metrics, namely, Quality of Train (QoT) Accuracy (Ac) and StorageCapacity (SC). While the Re-P scheme is only capable of separating classes that are linearly separable,Re-GAPSO one is a dynamic method, appropriate for constantly changing problems of the real life. Onthe other hand, GA-ANN is a Wrapper method and despite Relief can adapt itself to the machine learningalgorithm. Meanwhile, Ro-P scheme is useful for analyzing vague and imprecise information and, unlikeGA-ANN, has less calculative costs. Among these five schemes, Ro-GAPSO is a more precise one, whichhas less calculative cost and does not become stuck in local minima. Experimental results show that Re-Poutperforms other proposed and existing methods in terms of computational time complexity. |
| format | Article |
| id | doaj-art-b46fb5bec7704c5884048a21f66e3dcd |
| institution | Kabale University |
| issn | 2588-2910 2588-2929 |
| language | English |
| publishDate | 2017-12-01 |
| publisher | Amirkabir University of Technology |
| record_format | Article |
| series | AUT Journal of Electrical Engineering |
| spelling | doaj-art-b46fb5bec7704c5884048a21f66e3dcd2025-08-20T03:31:46ZengAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-29102588-29292017-12-0149222323210.22060/eej.2017.12151.50461960Combination of Feature Selection and Learning Methods for IoT Data FusionV. Sattari-Naeini0Zahra Parizi-Nejad1Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, IranDept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, IranIn this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessingthe data set based on curve fitting, reducing the data dimension and identifying the most effective featuresets according to data correlation, training classification algorithms, and finally predicting new databased on classification algorithms. The results derived from five compound schemes are investigated andcompared with each other with three metrics, namely, Quality of Train (QoT) Accuracy (Ac) and StorageCapacity (SC). While the Re-P scheme is only capable of separating classes that are linearly separable,Re-GAPSO one is a dynamic method, appropriate for constantly changing problems of the real life. Onthe other hand, GA-ANN is a Wrapper method and despite Relief can adapt itself to the machine learningalgorithm. Meanwhile, Ro-P scheme is useful for analyzing vague and imprecise information and, unlikeGA-ANN, has less calculative costs. Among these five schemes, Ro-GAPSO is a more precise one, whichhas less calculative cost and does not become stuck in local minima. Experimental results show that Re-Poutperforms other proposed and existing methods in terms of computational time complexity.https://eej.aut.ac.ir/article_1960_5b7511e4f87d3b6a9eb1a6bc95cececc.pdfinternet of thingsdata fusionrough set theoryperceptrongapso |
| spellingShingle | V. Sattari-Naeini Zahra Parizi-Nejad Combination of Feature Selection and Learning Methods for IoT Data Fusion AUT Journal of Electrical Engineering internet of things data fusion rough set theory perceptron gapso |
| title | Combination of Feature Selection and Learning Methods for IoT Data Fusion |
| title_full | Combination of Feature Selection and Learning Methods for IoT Data Fusion |
| title_fullStr | Combination of Feature Selection and Learning Methods for IoT Data Fusion |
| title_full_unstemmed | Combination of Feature Selection and Learning Methods for IoT Data Fusion |
| title_short | Combination of Feature Selection and Learning Methods for IoT Data Fusion |
| title_sort | combination of feature selection and learning methods for iot data fusion |
| topic | internet of things data fusion rough set theory perceptron gapso |
| url | https://eej.aut.ac.ir/article_1960_5b7511e4f87d3b6a9eb1a6bc95cececc.pdf |
| work_keys_str_mv | AT vsattarinaeini combinationoffeatureselectionandlearningmethodsforiotdatafusion AT zahraparizinejad combinationoffeatureselectionandlearningmethodsforiotdatafusion |