Credit Risk Evaluation Using a New Classification Model: L1-LS-SVM

2013 ◽  
Vol 321-324 ◽  
pp. 1917-1920
Author(s):  
Li Wei Wei ◽  
Qiang Xiao ◽  
Ying Zhang ◽  
Xiong Fei Ji
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Classification Model ◽  
Support Vector ◽  
New Classification ◽  
Robust Solution ◽  
Vector Machines ◽  
Feasibility Region ◽  
Testing Speed ◽  
Standard Support

Least squares support vector machine (LS-SVM) has an outstanding advantage of lower computational complexity than that of standard support vector machines. Its shortcomings are the loss of sparseness and robustness. Thus it usually results in slow testing speed and poor generalization performance. In this paper, a least squares support vector machine with L1 penalty (L1-LS-SVM) is proposed to deal with above shortcomings. A minimum of 1-norm based object function is chosen to get the sparse and robust solution based on the idea of basis pursuit (BP) in the whole feasibility region. Some UCI datasets are used to demonstrate the effectiveness of this model. The experimental results show that L1-LS-SVM can obtain a small number of support vectors and improve the generalization ability of LS-SVM.

scholarly journals A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Lu Xu ◽  
Qiong Shi ◽  
Bang-Cheng Tang ◽  
Shunping Xie
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Mercury Level ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Support Vector ◽  
Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

scholarly journals A Novel Sparse Least Squares Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao-Lei Xia ◽  
Weidong Jiao ◽  
Kang Li ◽  
George Irwin
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Regularization Parameter ◽  
Support Vector ◽  
Training Algorithm ◽  
Least Squares Approximation ◽  
Generalization Ability ◽  
Vector Machines ◽  
Training Cost ◽  
Benchmark Datasets

The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness. The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function. This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm—the Forward Least Squares Approximation SVM (FLSA-SVM). A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost. The FLSA-SVMs can also detect the linear dependencies in vectors of the input Gramian matrix. These attributes together contribute to its extreme sparseness. Experiments on benchmark datasets are presented which show that, compared to various SVM algorithms, the FLSA-SVM is extremely compact, while maintaining a competitive generalization ability.

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

2018 ◽  
Vol 18 (3) ◽  
pp. 715-724 ◽  
Author(s):  
Xiao Li ◽  
Xin Liu ◽  
Clyde Zhengdao Li ◽  
Zhumin Hu ◽  
Geoffrey Qiping Shen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Data Processing ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Displacement Monitoring ◽  
Processing Efficiency ◽  
Foundation Pit ◽  
Vector Machines

Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.

scholarly journals Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lean Yu
Keyword(s):  
Support Vector Machine ◽  
Computational Complexity ◽  
Credit Risk ◽  
Least Squares ◽  
Risk Evaluation ◽  
Least Squares Method ◽  
Support Vector ◽  
Generalization Capability ◽  
Fuzzy Support Vector Machine ◽  
Vector Machines

A least squares fuzzy support vector machine (LS-FSVM) model that integrates advantages of fuzzy support vector machine (FSVM) and least squares method is proposed for credit risk evaluation. In the proposed LS-FSVM model, the purpose of incorporating the concepts of fuzzy sets is to add generalization capability and outlier insensitivity, while the least squares method is adopted to reduce the computational complexity. For illustrative purposes, a real-world credit risk dataset is used to test the effectiveness and robustness of the proposed LS-FSVM methodology.

scholarly journals Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4059 ◽  
Author(s):  
Benny Chitambira ◽  
Simon Armour ◽  
Stephen Wales ◽  
Mark Beach
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Outage Probability ◽  
Least Squares ◽  
Ray Tracing ◽  
Direct Method ◽  
Support Vector ◽  
Experimental Setup ◽  
Vector Machines

This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better.

MECHANICAL FAULT RECOGNITION RESEARCH BASED ON LMD-LSSVM

2016 ◽  
Vol 40 (4) ◽  
pp. 541-549
Author(s):  
Zengshou Dong ◽  
Zhaojing Ren ◽  
You Dong
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Linear Interpolation ◽  
Experimental Result ◽  
Support Vector ◽  
Local Mean Decomposition ◽  
Fault Recognition ◽  
Vector Machines ◽  
Local Mean

Mechanical fault vibration signals are non-stationary, which causes system instability. The traditional methods are difficult to accurately extract fault information and this paper proposes a local mean decomposition and least squares support vector machine fault identification method. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.

scholarly journals A rolling bearing fault diagnosis method based on LSSVM

2020 ◽  
Vol 12 (1) ◽  
pp. 168781401989956
Author(s):  
Xuejin Gao ◽  
Hongfei Wei ◽  
Tianyao Li ◽  
Guanglu Yang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Kernel Function ◽  
Dimensional Space ◽  
Rolling Bearing ◽  
Classification Model ◽  
Support Vector ◽  
Bearing Fault ◽  
Diagnosis Method

The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

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