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

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. T...

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Bibliographic Details
Published in:Applied sciences 2019-03, Vol.9 (5), p.895
Main Authors: AL-Khaleefa, Ahmed, Ahmad, Mohd, Isa, Azmi, Esa, Mona, AL-Saffar, Ahmed, Hassan, Mustafa
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer engineering
cyclic dynamics
Datasets
extreme learning machine
Feature Adaptive
International conferences
Internet
Intrusion detection systems
Knowledge
knowledge preservation
Learning algorithms
Literature reviews
Localization
Machine learning
Neural networks
online learning
Pattern recognition
Retraining
Software
Transfer learning
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9050895