Al-Khaleefa, Ahmed Salih and Mohd Riduan, Ahmad and Azmi Awang, Md Isa and Mona Riza, Mohd Esa and Al-Saffar, Ahmed Ali Mohammed and Hassan, Mustafa Hamid (2019) Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine. Applied Sciences, 9 (5). pp. 1-17. ISSN 2076-3417. (Published)
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Abstract
Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively
| Item Type: | Article |
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| Uncontrolled Keywords: | Online learning; extreme learning machine; cyclic dynamics; transfer learning; knowledge preservation; Feature Adaptive |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Noorul Farina Arifin |
| Date Deposited: | 12 Mar 2019 01:28 |
| Last Modified: | 12 Mar 2019 01:28 |
| URI: | http://umpir.ump.edu.my/id/eprint/24447 |
| Download Statistic: | View Download Statistics |
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