Pallet-level classification using principal component analysis in ensemble learning model

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)

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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.

Item Type: Article
Uncontrolled Keywords: Pallet-level; RFID; Ensemble Learning; Features Selection; RSSI
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 01 Apr 2022 07:26
Last Modified: 01 Apr 2022 07:26
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