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

Ahmed Al-Khaleefa,Ahmed Al-Saffar,Mona Esa,Mustafa Hassan,Azmi Isa,Mohd Ahmad

doi:10.3390/app9050895

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.

Highlights

Machine learning has a wide range of applications in the era of artificial intelligence
Cyclic dynamic generator (CDG) results were generated for three datasets, wherein two datasets; TampereU and UJIndoorLoc, belonged to Wi-Fi localization; and one belonged to other machine learning areas; KDD99 from cloud security
Cyclic dynamics is a common type of dynamics that occurs in time series data

Summary

Introduction

Machine learning has a wide range of applications in the era of artificial intelligence. Massive data generation and storage facilitate the extraction and transfer of useful knowledge from data, enabling machines to become as smart as humans. Examples of machine learning-based extraordinary technologies include autonomous cars [1], biometric based human identification [2], time series forecasting in different domains [3], security [4], and computer vision [5]. The neural network uses a mathematical structure to gain and store knowledge, which is relevant to machine learning. Neural networks can be used for prediction and classification. The classical approach of training neural networks is to provide labeled data, and the use of training algorithms such as backpropagation [6] and machines’ extreme learning [7].

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Conclusion