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Laplacian Support Vector Machines as Data Classifier in Machine learning Approaches of Structural Health Monitoring

JOURNAL OF EARTHQUAKE AND TSUNAMI 2025年第1期19卷

作者： Fazeli, Hassan Hassani, Nemat Safi, Mohammad Shahid Beheshti Univ Civil Engn Coll Abbaspour Tech Campus Tehran Iran

With the progress of technologies in data collection systems, structural engineers are faced with a large amount of unlabeled data gathered from various states of different structures, directing studies toward using improved classifiers in machine learning approaches of structural health monitoring. In this research, Laplacian Support Vector Machines (LapSVMs) are used as a semi-supervised learning algorithm to classify mentioned data. Using vibrational data of structure, dynamic properties of the structure are extracted. Modal strain energy is used as damage sensitive features (DSF) to perform damage assessment in a statistical pattern recognition framework by a semi-supervised learning algorithm using LapSVMs to classify unlabeled and labeled data. Also, Support Vector Machines (SVMs) and Regularized Least Square Classifier (RLSC) are used as classifiers to compare results. To compare the effectiveness of the proposed algorithm, different states of structural response are determined by labeled and unlabeled data. These results show high accuracy of LapSVM methods compared to others in cases where the labeled dataset is small.

关键词： Statistical pattern recognition support vector machines method semi-supervised learning algorithms system identification

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Label propagation based on local information with adaptive determination of number and degree of neighbor's similarity

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NEUROCOMPUTING 2015年 153卷 41-53页

作者： Saffari, Seyed Alireza Ebrahimi-Moghadam, Abbas Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran

In many practical applications of machine vision, a small number of samples are labeled and therefore, classification accuracy is low. On the other hand, labeling by humans is a very time consuming process, which requires a degree of proficiency. semi-supervised learning algorithms may be used as a proper solution in these situations, where epsilon-neighborhood or k nearest neighborhood graphs are employed to build a similarity graph. These graphs, on one hand, have a high degree of sensitivity to noise. On the other hand, optimal determination of epsilon and k parameters is a complex task. In some classification algorithms, sparse representation (SR) is employed in order to overcome these obstacles. Although SR has its own advantages, SR theory in its coding stage does not reflect local information and it requires a time consuming and heavy optimization process. Locality-constrained Linear Coding (LLC) addresses these problems and regards the local information in the coding process. In this paper we examine the effectiveness of using local information in form of label propagation algorithm and present three new label propagation modifications. Experimental results on three UCI datasets, two face databases and a biometric database show that our proposed algorithms have higher classification rates compared to other competitive algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： semi-supervised learning algorithms Graph-based algorithms Sparse representation Local information

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