A review on extreme learning machine

Wang, Jian; Lu, Siyuan; Wang, Shuihua; Zhang, Yudong

doi:10.1007/s11042-021-11007-7

Cited by 237 publications

(104 citation statements)

References 241 publications

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“…The extreme learning machine (ELM) is a single-hidden layer feedforward neural network training algorithm that produces results much quicker than traditional approaches and generates excellent performance [88]. Guang-Bin and Qin-Yu proposed the extreme learning machine (ELM) with the purpose of training single-hidden layer feedforward networks [89].…”

Section: Extreme Learning Machinementioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

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Section: Extreme Learning Machinementioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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“…Few works propose the use of one-class models, and even fewer propose a comparison across multiple datasets, as we propose in this paper. For example, in [17], [18] the use of Extreme Learning Machines (ELM) is proposed as an alternative to Auto-Encoders based on artificial neural networks, with the aim of decreasing learning times, memory requirements for saving weights in memory and computational complexity, in the sense of waiting times during the processing of new observations. The main problem with ELM models is that the transformation matrix is based on random processes that often do not apply, with a strong dependency on the analysed dataset.…”

Section: B Related Workmentioning

confidence: 99%

Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Dini

Begni

Ciavarella³

et al. 2022

IEEE Access

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This work exploits the concept of one-class classifier applied to the problem of anomaly detection in communication networks. The article presents the design of an innovative anomaly detection algorithm based on polynomial interpolation technique and statistical analysis. The innovative method is applied to datasets largely used in the scientific community for bench-marking such as KDD99, UNSW-NB15 and CSE-CIC-IDS-2018, and further evaluated with application to a novel available dataset EDGE-IIOTSET 2022. The paper also reports experimental results showing that the proposed methodology outperforms classic one-class classifiers, such as Extreme Learning Machine and Support Vector Machine models, and rule-based intrusion detection system like SNORT. With respect to binary classifiers, this work has the advantage of not requiring any a-priori knowledge about attacks and is based on the collection of only normal data traffic.

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“…This approach was initially proposed in [1], and it introduced the concept that the input weight and bias values in the hidden layer are allocated in a random fashion, while the output weight values are computed by utilizing the Moore-Penrose (MP) pseudo inverse [2]. ELMs have shown excellent generalization capabilities [3], and they are known to be very fast and efficient due to the fact that they do not require traditional training, which is one of the most time-consuming tasks when dealing with other types of neural networks. By different training, we mean that ELM models learn without tuning hidden parameters in several iterations, and the only parameter that needs to be determined is the weight between the hidden layer and the output layer, using MP, as mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Bačanin

Stoean

Živković

et al. 2022

Sensors

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There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

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A review on extreme learning machine

Cited by 237 publications

References 241 publications

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

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