Infinite-Term Memory Classifier for Wi-Fi Localization Based on Dynamic Wi-Fi Simulator

Al-Khaleefa, Ahmed Salih; Ahmad, Mohd Riduan; Isa, Azmi Awang Md; Esa, Mazlina; Al-Saffar, Ahmed; Aljeroudi, Yazan

doi:10.1109/access.2018.2870754

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…However, the computational load increases as the number of features peaks. This drawback was addressed by [41] and by attaching an external memory (EM) to the OSELM. The EM preserves knowledge specific to the old non-active features and restores knowledge specific to new active features.…”

Section: Related Workmentioning

confidence: 99%

“…For feature adaptive online sequential extreme learning (FA-OSELM), transfer learning is useful for transferring knowledge related to active features in the current and previous NNs; this process causes knowledge loss while the NNs transform. Two novel approaches, namely, knowledge preserving OSELM (KP-OSELM) [40] and infinite term memory OSELM (ITM-OSELM) [41], were proposed in previous research. In KP-OSELM, the NN is fixed with many inputs equal to the total number of features with the use of an encoding approach for non-active features.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Online Machine Learning Models Based on Cyclic Dynamic and Feature-Adaptive Time Series

Al-Khaleefa

Hassan

Ahmad

et al. 2021

IEICE Trans. Inf. & Syst.

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Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent learning need to be addressed and solved. One most important challenge is the non-steady performance of various machine learning models during online learning and operation. Online learning is the ability of a machine-learning model to modernize information without retraining the scheme when new information is available. To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving OSELM (KP-OSELM), and Infinite Term Memory OSELM (ITM-OSELM). Specifically, we provide a testbed for the models by building a framework and configuring various evaluation scenarios given different factors in the topological and mathematical aspects of the models. Furthermore, we generate different characteristics of the time series to be learned. Results prove the real impact of the tested parameters and scenarios on the models. In terms of accuracy, KP-OSELM and ITM-OSELM are superior to OSELM and FA-OSELM. With regard to time efficiency related to the percentage of decreases in active features, ITM-OSELM is superior to KP-OSELM.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Online Machine Learning Models Based on Cyclic Dynamic and Feature-Adaptive Time Series

Al-Khaleefa

Hassan

Ahmad

et al. 2021

IEICE Trans. Inf. & Syst.

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“…The work conducted in Ref. [14] describes a novel type of extreme learning machine with the capability of preserving older knowledge, using external memory and transfer learning; ITM-OSELM. In this study, the authors applied the concept to Wi-Fi localization and showed good performance improvement in the context of cyclic dynamic and feature adaptability of Wi-Fi navigation.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine

et al. 2019

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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.

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“…One particular instance concerning WiFi positioning is when the users navigate frequently between the same areas. The work conducted in [24,25] describes a novel type of extreme learning machine using external memory and transfer learning: ITM-OSELM. In these studies, the authors applied ITM-OSELM to WiFi localization, and showed a good improvement in performance in the context of the cyclic dynamic and feature adaptability of WiFi navigation.…”

Section: Related Workmentioning

confidence: 99%

MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System

et al. 2019

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Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. The most difficult aspect of techniques based on fingerprints is the effect of dynamic environmental changes on fingerprint authentication. With the aim of dealing with this problem, many experts have adopted transfer-learning methods, even though in WiFi indoor localization the dynamic quality of the change in the fingerprint has some cyclic factors that necessitate the use of previous knowledge in various situations. Thus, this paper presents the maximum feature adaptive online sequential extreme learning machine (MFA-OSELM) technique, which uses previous knowledge to handle the cyclic dynamic factors that are brought about by the issue of mobility, which is present in internal environments. This research extends the earlier study of the feature adaptive online sequential extreme learning machine (FA-OSELM). The results of this research demonstrate that MFA-OSELM is superior to FA-OSELM given its capacity to preserve previous data when a person goes back to locations that he/she had visited earlier. Also, there is always a positive accuracy change when using MFA-OSELM, with the best change achieved being 27% (ranging from eight to 27% and six to 18% for the TampereU and UJIIndoorLoc datasets, respectively), which proves the efficiency of MFA-OSELM in restoring previous knowledge.

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Infinite-Term Memory Classifier for Wi-Fi Localization Based on Dynamic Wi-Fi Simulator

Cited by 6 publications

References 11 publications

Performance Evaluation of Online Machine Learning Models Based on Cyclic Dynamic and Feature-Adaptive Time Series

Performance Evaluation of Online Machine Learning Models Based on Cyclic Dynamic and Feature-Adaptive Time Series

Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine

MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System

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