Adaptive Safe Semi-Supervised Extreme Machine Learning

Ma, Jun; Yuan, Chao

doi:10.1109/access.2019.2922385

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…However, the disadvantages include the need for a large amount of unlabelled data and the difficulty of choosing appropriate clustering techniques. [3] 4) "Fake News Detection using Hybrid Naive Bayes and SVM Classifier" by V. M. Borole and A. S. Deshpande. The approach used in this paper is a hybrid approach that combines Naive Bayes and Support Vector Machine (SVM) classifiers for the detection of fake news.…”

Section: IImentioning

confidence: 99%

Textual Analysis and Identification of Spam News

R¹,

Rohith²,

Dinesh³

et al. 2023

IJRASET

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Our study aims to tackle the problem of recognizing spam news through the use of Machine Learning. To accomplish this, we devised a hybrid approach named "SpamBuster" that employs various algorithms including Bernoulli Naive Bayes, Multinomial Naive Bayes, Random Forest, and Kernel SVM. We trained the model on a large dataset of news articles and evaluated it based on multiple performance metrics such as accuracy, precision, recall, and F1 score. Our experimental findings revealed that our approach achieved outstanding performance with an accuracy of 0.9507, precision of 0.9747, recall of 0.9253, and F1 score of 0.9494. These results demonstrate that our approach is effective in identifying spam news and could have practical implications in combating the spread of false information and propaganda. In conclusion, our research showcases the potential of machine learning techniques and textual analysis for detecting spam news and emphasizes the significance of this area of research in the modern era of information.

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Section: IImentioning

confidence: 99%

Textual Analysis and Identification of Spam News

R¹,

Rohith²,

Dinesh³

et al. 2023

IJRASET

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“…C 0 = 10 6−i (37) λ = 10 6−j (38) where i and j are integers between [0, 11]. JRSSELM and SSELM maintain consistency in parameter settings.…”

Section: Hybrid Msmpa and Joint Regularized Semi-supervised Extreme Learning Machinementioning

confidence: 99%

“…To enhance the feature extraction and classification performance of SSELM, She et al [37] proposed a new hierarchical semi-supervised extremal learning machine (HSSELM) that uses the HELM method for automatic feature extraction of deep structures and then uses SSELM for classification tasks. To address the problem that manifold graph are only pre-constructed before classification and not changed later, which leads to poor model performance robustness, Ma et al [38] proposed an adaptive safety semi-supervised extreme learning machine, which allows the model to adaptively compute the safety of unlabeled samples and adaptively construct manifold graphs. To address the problem that SSELM is sensitive to outliers in labeled samples, Pei et al [39] proposed a new robust SSELM that uses a nonconvex squared loss function to impose a constant penalty on outliers and mitigate their possible negative effects.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Yang

Xia

et al. 2021

Mathematics

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A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

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“…Supervised ELMs need numerous labeled data to ensure its high performance, such as Kernel Extreme Learning Machine (KELM) [19], Weighted Extreme Learning Machine (WELM) [20], Twin Extreme Machines (TELM) [21], and Adaptive Regularized Extreme Learning Machine (A-RELM) [22]. Semi-supervised ELM usually requires unlabeled data together with labeled data to train models well, including Laplacian Twin Extreme Learning Machine (Lap-TELM) [23], Semi-Supervised Extreme Learning Machine (SS-ELM) [24], Robust Semi-Supervised Extreme Learning Machine (RSS-ELM) [25], and Adaptive Safe Semi-Supervised Extreme Learning Machine (AdSafe-SSELM) [26]. In some other cases where no labeled data are available, some Unsupervised ELM (USELM) algorithms are proposed for clustering, dimension reduction, or data representation, such as Unsupervised Extreme Learning Machine (USELM) [24], Extreme Learning Machine as an Auto-Encoder (ELM-AE) [27], Enhanced Unsupervised Extreme Learning Machine (EUELM) [28], and Unsupervised Feature Selection based Extreme Learning Machine (UFSELM) [29].…”

Section: Introductionmentioning

confidence: 99%

Discriminative Extreme Learning Machine with Cross‐Domain Mean Approximation for Unsupervised Domain Adaptation

Zang

et al. 2022

Complexity

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Extreme Learning Machine (ELM) is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation (DA) in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation (DELM-CDMA) for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation (CDMA) into the hidden layer of ELM to reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis (LDA) is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation (DKELM-CDMA) as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches.

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Adaptive Safe Semi-Supervised Extreme Machine Learning

Cited by 13 publications

References 31 publications

Textual Analysis and Identification of Spam News

Textual Analysis and Identification of Spam News

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Discriminative Extreme Learning Machine with Cross‐Domain Mean Approximation for Unsupervised Domain Adaptation

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