scholarly journals Investigation of the Impact of Data Comparability on Performance of Support Vector Machine Models for Credit Scoring

2015 ◽  
Vol 9 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Yanwen DONG ◽  
Xiying HAO ◽  
Hideo SATO
Keyword(s):  
Support Vector Machine ◽  
Credit Scoring ◽  
Support Vector ◽  
Data Comparability ◽  
The Impact

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

scholarly journals Study on Crash Injury Severity Prediction of Autonomous Vehicles for Different Emergency Decisions Based on Support Vector Machine Model

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 381 ◽  
Author(s):  
Yaping Liao ◽  
Junyou Zhang ◽  
Shufeng Wang ◽  
Sixian Li ◽  
Jian Han
Keyword(s):  
Support Vector Machine ◽  
Autonomous Vehicles ◽  
Injury Severity ◽  
Prediction Models ◽  
Motor Vehicle ◽  
Support Vector ◽  
Severity Prediction ◽  
Emergency Decision ◽  
The Impact ◽  
Crash Injury Severity

Motor vehicle crashes remain a leading cause of life and property loss to society. Autonomous vehicles can mitigate the losses by making appropriate emergency decision, and the crash injury severity prediction model is the basis for autonomous vehicles to make decisions in emergency situations. In this paper, based on the support vector machine (SVM) model and NASS/GES crash data, three SVM crash injury severity prediction models (B-SVM, T-SVM, and BT-SVM) corresponding to braking, turning, and braking + turning respectively are established. The vehicle relative speed (REL_SPEED) and the gross vehicle weight rating (GVWR) are introduced into the impact indicators of the prediction models. Secondly, the ordered logit (OL) and back propagation neural network (BPNN) models are established to validate the accuracy of the SVM models. The results show that the SVM models have the best performance than the other two. Next, the impact of REL_SPEED and GVWR on injury severity is analyzed quantitatively by the sensitivity analysis, the results demonstrate that the increase of REL_SPEED and GVWR will make vehicle crash more serious. Finally, the same crash samples under normal road and environmental conditions are input into B-SVM, T-SVM, and BT-SVM respectively, the output results are compared and analyzed. The results show that with other conditions being the same, as the REL_SPEED increased from the low (0–20 mph) to middle (20–45 mph) and then to the high range (45–75 mph), the best emergency decision with the minimum crash injury severity will gradually transition from braking to turning and then to braking + turning.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine

2020 ◽  
Vol 19 (6) ◽  
pp. 2075-2090 ◽  
Author(s):  
Hao Cheng ◽  
Furui Wang ◽  
Linsheng Huo ◽  
Gangbing Song
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Rapid Development ◽  
Voice Recognition ◽  
Support Vector ◽  
Machine Model ◽  
Frequency Scale ◽  
Removal Time ◽  
The Impact ◽  
Non Destructive

Deposits prevention and removal in pipeline has great importance to ensure pipeline operation. Selecting a suitable removal time based on the composition and mass of the deposits not only reduces cost but also improves efficiency. In this article, we develop a new non-destructive approach using the percussion method and voice recognition with support vector machine to detect the sandy deposits in the steel pipeline. Particularly, as the mass of sandy deposits in the pipeline changes, the impact-induced sound signals will be different. A commonly used voice recognition feature, Mel-Frequency Cepstrum Coefficients, which represent the result of a cosine transform of the real logarithm of the short-term energy spectrum on a Mel-frequency scale, is adopted in this research and Mel-Frequency Cepstrum Coefficients are extracted from the obtained sound signals. A support vector machine model was employed to identify the sandy deposits with different mass values by classifying energy summation and Mel-Frequency Cepstrum Coefficients. In addition, the classification accuracies of energy summation and Mel-Frequency Cepstrum Coefficients are compared. The experimental results demonstrated that Mel-Frequency Cepstrum Coefficients perform better in pipeline deposits detection and have great potential in acoustic recognition for structural health monitoring. In addition, the proposed Mel-Frequency Cepstrum Coefficients–based pipeline deposits monitoring model can estimate the deposits in the pipeline with high accuracy. Moreover, compared with current non-destructive deposits detection approaches, the percussion method is easy to implement. With the rapid development of artificial intelligence and acoustic recognition, the proposed method can realize higher accuracy and higher speed in the detection of pipeline deposits, and has great application potential in the future. In addition, the proposed percussion method can enable robotic-based inspection for large-scale implementation.

scholarly journals Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5207
Author(s):  
Hyun-Taik Oh ◽  
Jong-Ick Won ◽  
Sung-Choong Woo ◽  
Tae-Won Kim
Keyword(s):  
Support Vector Machine ◽  
Vinylidene Fluoride ◽  
Impact Damage ◽  
Low Velocity Impact ◽  
Electrical Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Low Velocity ◽  
Damage State ◽  
The Impact

Carbon fiber reinforced plastics (CFRPs) have high specific stiffness and strength, but they are vulnerable to transverse loading, especially low-velocity impact loadings. The impact damage may cause serious strength reduction in CFRP structure, but the damage in a CFRP is mainly internal and microscopic, that it is barely visible. Therefore, this study proposes a method of determining impact damage in CFRP via poly(vinylidene fluoride) (PVDF) sensor, which is convenient and has high mechanical and electrical performance. In total, 114 drop impact tests were performed to investigate on impact responses and PVDF signals due to impacts. The test results were analyzed to determine the damage of specimens and signal features, which are relevant to failure mechanisms were extracted from PVDF signals by means of discrete wavelet transform (DWT). Support vector machine (SVM) was used for optimal classification of damage state, and the model using radial basis function (RBF) kernel showed the best performance. The model was validated through a 4-fold cross-validation, and the accuracy was reported to be 92.30%. In conclusion, impact damage in CFRP structures can be effectively determined using the spectral analysis and the machine learning-based classification on PVDF signals.

scholarly journals Real-time flood forecast using the coupling support vector machine and data assimilation method

2013 ◽  
Vol 16 (5) ◽  
pp. 973-988 ◽  
Author(s):  
Xiao-Li Li ◽  
Haishen Lü ◽  
Robert Horton ◽  
Tianqing An ◽  
Zhongbo Yu
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Scale ◽  
Support Vector ◽  
Flood Forecast ◽  
Risk Minimization ◽  
Svm Model ◽  
The Impact ◽  
Assimilation Time ◽  
Better Than

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

A Research of GPS Height Fitting in Mountainous Terrain by CPSO Optimization FLS-SVM

2013 ◽  
Vol 336-338 ◽  
pp. 2339-2343 ◽  
Author(s):  
Yong Gan ◽  
Xin Xin Liu ◽  
Yuan Pan Zheng
Keyword(s):  
Support Vector Machine ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Mountainous Area ◽  
Machine Model ◽  
Normal Height ◽  
Fitting Method ◽  
Fitting In ◽  
The Impact

For the problem of data limited in the mountainous area, a method of FLS-SVM (Fuzzy Least Square Vector Machine) that supporting small sample data and having high noise ability was put forward. The CPSO(chaos particle swarm optimization algorithm) is adopted to optimize the parameters of least squares support vector machine algorithm, and to avoid the uncertainty of artificial parameter selection. Meanwhile, considering the impact of terrain, the terrain correction is introduced to the support vector machine model. The experimental results show that the model can get higher precision fitting effect compared with traditional fitting method such as PSO-LSSVM and GA-LSSVM, and suitable for the SRTM application of getting normal height.

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