Robust and Lightweight Ensemble Extreme Learning Machine Engine Based on Eigenspace Domain for Compressed Learning

Li, Huai-Ting; Chou, Ching-Yao; Chen, Yi-Ta; Wang, Shenghui

doi:10.1109/tcsi.2019.2940642

Cited by 26 publications

(11 citation statements)

References 28 publications

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“…This type of method is difficult to obtain a learning rate, and it is easy to fall into the local optimum and the amount of iteration is very large [35], hence a long delay. According to literature [37]- [41], the kernel-based extreme learning machine can effectively solve the problems of BP neural network, and has a strong classification ability. Therefore, this paper uses the kernel-based extreme learning machine instead of BP algorithm to achieve supervised classification.…”

Section: Network Modelmentioning

confidence: 99%

“…Compared with traditional neural networks, ELM improves the learning speed while maintaining good generalization capabilities of the network, and has strong nonlinear fitting capabilities, which can effectively reduce the amount of calculation as well as the search space. Based on the above advantages, ELM has been applied in many fields, such as information collection [37], big data application [38], logo recognition [39], language recognition [40]. Kernel-based Extreme Learning Machine [41] combines the kernel function on the basis of ELM.…”

Section: B Kernel-based Extreme Learning Machinementioning

confidence: 99%

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Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

et al. 2021

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Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve the detection accuracy and performance, we proposed an intrusion detection model based on improved deep belief network (DBN). Traditional neural network training methods, like Back Propagation (BP), start to train a model with preset parameters such as the randomly initialized weights and thresholds, which may bring some issues, e.g., attracting the model to the local optimal solutions, or requiring a long training period. We use the Kernel-based Extreme Learning Machine (KELM) with the supervised learning ability to replace the BP algorithm in DBN in a bid to ameliorate the situation. Considering the problem of poor classification performance usually caused by randomly initializing kernel parameters with KELM, an enhanced grey wolf optimizer (EGWO) is designed to optimize the parameters of KELM. In order to improve the search ability and optimization ability of the traditional grey wolf optimizer algorithm, a novel optimization strategy combining the inner and outer hunting is introduced. Experiments on KDDCup99, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets show that the proposed DBN-EGWO-KELM algorithm has greater advantages in terms of its accuracy, precision, true positive rate, false positive rate and other evaluation indices compared with BP, RBF, SVM, KELM, LIBSVM, CNN, DBN-KELM and other intrusion detection models, and can effectively meet the requirements of intrusion detection of complex networks.INDEX TERMS Intrusion detection, deep belief network, kernel-based extreme learning machine, grey wolf optimizer.

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Section: Network Modelmentioning

confidence: 99%

Section: B Kernel-based Extreme Learning Machinementioning

confidence: 99%

Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

et al. 2021

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“…Pixel-wise lifetime recovery has been widely used, since it is consistent with the sensor readout and more computationally economical than 3-D algorithms. The extreme learning machine (ELM) [ 31 ] is an efficient algorithm to process 1-D signals for biological applications, such as electrocardiogram (ECG) and electroencephalogram (EEG) signals [ 32 ]. Inspired by related literature, we used ELM to reconstruct lifetimes from 1-D histograms using multi-variable regression.…”

Section: Introductionmentioning

confidence: 99%

Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine

Zang

Xiao

Wang

et al. 2022

Sensors

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We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

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“…Compared with support vector machine (SVM) [23], ELM tends to yield better classification performance with less optimization constrains [55]. Due to its superior training speed and good generalization capability [82], ELM is widely applied in a variety of learning problems, such as classification, regression, clustering, and feature mapping. ELM evolved as many variants have been proposed to further improve its stability and generalization for specific applications [29,61,86,223].…”

Section: Introductionmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

236

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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Robust and Lightweight Ensemble Extreme Learning Machine Engine Based on Eigenspace Domain for Compressed Learning

Cited by 26 publications

References 28 publications

Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine

A review on extreme learning machine

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