A Voting-Near-Extreme-Learning-Machine Classification Algorithm

Hou, Huayan; Meng, Qing-Hao; Zhang, Xiao-Nei

doi:10.1109/icpr.2018.8545217

2018 24th International Conference on Pattern Recognition (ICPR) 2018

DOI: 10.1109/icpr.2018.8545217

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A Voting-Near-Extreme-Learning-Machine Classification Algorithm

Huayan Hou

Qing-Hao Meng

Xiao-Nei Zhang

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Cited by 3 publications

(1 citation statement)

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“…Accuracy: The proportion of samples predicted to be correct. To prove the superiority of our proposed algorithm, we compare our algorithm with the ELM algorithm [21], VNELM algorithm [28], PCA-ELM algorithm [29], LDA-ELM algorithm, and EGRNN algorithm [20]. The experiment is also performed in terms of efficiency, accuracy, detection rate, and false detection rate.…”

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confidence: 99%

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Zheng

Zhou

Wang

et al. 2020

Sensors

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The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.

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confidence: 99%

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Zheng

Zhou

Wang

et al. 2020

Sensors

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Smart Electronic Nose Enabled by an All‐Feature Olfactory Algorithm

Fang

et al. 2022

Advanced Intelligent Systems

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An electronic nose (e‐nose) mimics the mammalian olfactory system in identifying odors and expands human olfaction boundaries by tracing toxins and explosives. However, existing feature‐based odor recognition algorithms rely on domain‐specific expertise, which may limit the performance due to information loss during the feature extraction process. Inspired by human olfaction, a smart electronic nose enabled by an all‐feature olfactory algorithm (AFOA) is proposed, whereby all features in a gas sensing cycle of semiconductor gas sensors, including the response, equilibrium, and recovery processes are utilized. Specifically, our method combines 1D convolutional and recurrent neural networks with channel and temporal attention modules to fully utilize complementary global and dynamic information. It is further demonstrated that a novel data augmentation method can transform the raw data into a suitable representation for feature extraction. Results show that the e‐nose simply comprising of six semiconductor gas sensors achieves superior performances to state‐of‐the‐art methods on the Chinese liquor data. Ablation studies reveal the contribution of each sensor in odor recognition. Therefore, a deep‐learning‐enabled codesign of sensor arrays and recognition algorithms can reduce the heavy demand for a huge amount of highly specialized gas sensors and provide interpretable insights into odor recognition dynamics in an iterative way.

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Design of Improved Version of Sigmoidal Function with Biases for Classification Task in ELM Domain

Mugunthan¹,

Vijayakumar²

2021

JSCP

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Extreme Learning Machine (ELM) is one of the latest trends in learning algorithm, which can provide a good recognition rate within less computation time. Therefore, the algorithm can sustain for a faster response application by utilizing a feed-forward neural network. In this research article, the ELM method has been designed with the presence of sigmoidal function of biases in the hidden nodes to perform the classification task. The classification task is very challenging with the existing learning algorithm and increased computation time due to the huge amount of dataset. While handling of the stochastic matrix for hidden layer, output provides the lower performance for learning rate and robustness in the determination. To address these issues, the modified version of ELM has been developed to obtain better accuracy and minimize the classification error. This research article includes the mathematical proof of sigmoidal activation function with biases of the hidden nodes present in the networks. The output matrix maintains the column rank in order to improve the speed of the training output weights (β). The proposed improved version of ELM leverages better accuracy and efficacy in classification and regression problems as well. Due to the inclusion of matrix column ranking in mathematical proof, the proposed improved version of ELM solves the slow training speed and over-fitting problems present in the existing learning approach.

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A Voting-Near-Extreme-Learning-Machine Classification Algorithm

Cited by 3 publications

References 20 publications

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Smart Electronic Nose Enabled by an All‐Feature Olfactory Algorithm

Design of Improved Version of Sigmoidal Function with Biases for Classification Task in ELM Domain

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