Pallet-level classification using principal component analysis in ensemble learning model
In this paper, we present a machine learning pipeline to solve a multiclass classification of radio frequency identification (RFID) signal strength. The goal is to identify ten pallet levels using nine statistical features derived from RFID signals and four various ensemble learning classification m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33608/1/Pallet%20level%20classification%20using%20principal%20component%20analysis.pdf |
| _version_ | 1825814239857606656 |
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| author | Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Muhammad Aizzat, Zakaria Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman |
| author_facet | Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Muhammad Aizzat, Zakaria Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman |
| author_sort | Choong, Chun Sern |
| collection | UMP |
| description | In this paper, we present a machine learning pipeline to solve a multiclass classification of radio frequency identification (RFID) signal strength. The goal is to identify ten pallet levels using nine statistical features derived from RFID signals and four various ensemble learning classification models. The efficacy of the models was evaluated by considering features that were dimensionally reduced via Principal Component Analysis (PCA) and original features. It was shown that the PCA reduced features could provide a better classification accuracy of the pallet levels in comparison to the selection of all features via Extra Tree and Random Forest models. |
| first_indexed | 2024-03-06T12:55:51Z |
| format | Article |
| id | UMPir33608 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:55:51Z |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir336082022-04-01T07:26:50Z http://umpir.ump.edu.my/id/eprint/33608/ Pallet-level classification using principal component analysis in ensemble learning model Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Muhammad Aizzat, Zakaria Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering In this paper, we present a machine learning pipeline to solve a multiclass classification of radio frequency identification (RFID) signal strength. The goal is to identify ten pallet levels using nine statistical features derived from RFID signals and four various ensemble learning classification models. The efficacy of the models was evaluated by considering features that were dimensionally reduced via Principal Component Analysis (PCA) and original features. It was shown that the PCA reduced features could provide a better classification accuracy of the pallet levels in comparison to the selection of all features via Extra Tree and Random Forest models. Penerbit UMP 2020-06 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33608/1/Pallet%20level%20classification%20using%20principal%20component%20analysis.pdf Choong, Chun Sern and Ahmad Fakhri, Ab. Nasir and Muhammad Aizzat, Zakaria and Anwar P. P., Abdul Majeed and Mohd Azraai, Mohd Razman (2020) Pallet-level classification using principal component analysis in ensemble learning model. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (1). pp. 23-27. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v2i1.6720 https://doi.org/10.15282/mekatronika.v2i1.6720 |
| spellingShingle | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Muhammad Aizzat, Zakaria Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman Pallet-level classification using principal component analysis in ensemble learning model |
| title | Pallet-level classification using principal component analysis in ensemble learning model |
| title_full | Pallet-level classification using principal component analysis in ensemble learning model |
| title_fullStr | Pallet-level classification using principal component analysis in ensemble learning model |
| title_full_unstemmed | Pallet-level classification using principal component analysis in ensemble learning model |
| title_short | Pallet-level classification using principal component analysis in ensemble learning model |
| title_sort | pallet level classification using principal component analysis in ensemble learning model |
| topic | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33608/1/Pallet%20level%20classification%20using%20principal%20component%20analysis.pdf |
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