Journal articles: 'Fuzzy Support Vector Machine (FSVM)'

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Zhang, Rui, Tong Bo Liu, and Ming Wen Zheng. "An New Fuzzy Support Vector Machine for Binary Classification." Advanced Materials Research 433-440 (January 2012): 2856–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2856.

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In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classiﬁcation in the three methods stated above.

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Zhou, Huaping, and Huangli Qin. "Self-Adjusting Fuzzy Support Vector Machine Based on Analysis of Potential Support Vector Sample Point." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (September 2019): 1959035. http://dx.doi.org/10.1142/s0218001419590353.

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Fuzzy support vector machine (FSVM) is a part of machine learning with its good classification effect. So far, there are two most commonly used FSVM models: FSVM on account of class core and fuzzy support vector machine on account of hyperplane that is over class core. Each has its own problems: FSVM on account of class core are dependent on the geometric shape of sample sets. Although FSVM on account of hyperplane that is over class core can solve the above problems to some extent. However, this algorithm has low generalization ability and high time complexity. Therefore, Inspired by these two common models, the paper proposes an improved membership function method. By analyzing and calculating the potential support vector sample points, adjustment factor is obtained, which drives the class core to adjust along the direction away from the outliers. In this way, membership of noise and outliers are reduced and the membership of support vector will also increase to some extent. In this paper, a new experimental comparison method is used, which can make the comparison of classification effect more obvious and convincing. The experimental part compares the proposed FSVM model with the above two FSVM models. It shows that the proposed algorithm improves the stability and classification accuracy to some extent.

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Shanmugapriya, P., and Y. Venkataramani. "Analysis of Speaker Verification System Using Support Vector Machine." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 10 (February 25, 2017): 6531–42. http://dx.doi.org/10.24297/jac.v13i10.5839.

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The integration of GMM- super vector and Support Vector Machine (SVM) has become one of most popular strategy in text-independent speaker verification system.Â This paper describes the application of Fuzzy Support Vector Machine (FSVM) for classification of speakers using GMM-super vectors. Super vectors are formed by stacking the mean vectors of adapted GMMs from UBM using maximum a posteriori (MAP). GMM super vectors characterize speakerâ€™s acoustic characteristics which are used for developing a speaker dependent fuzzy SVM model. Introducing fuzzy theory in support vector machine yields better classification accuracy and requires less number of support vectors. Experiments were conducted on 2001 NIST speaker recognition evaluation corpus. Performance of GMM-FSVM based speaker verification system is compared with the conventional GMM-UBM and GMM-SVM based systems.Â Experimental results indicate that the fuzzy SVM based speaker verification system with GMM super vector achieves better performance to GMM-UBM system. Â

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Ke, Hong Xia, Guo Dong Liu, and Guo Bing Pan. "Fuzzy Support Vector Machine for PolSAR Image Classification." Advanced Materials Research 639-640 (January 2013): 1162–67. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.1162.

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Fully Polarimetric Synthetic Aperture Radar (PolSAR) image classification, with the complexity for its data’s scattering mechanism and statistical property, has expected to be performed by an automatic categorization. This paper presents a supervised method called Fuzzy support vector machine (FSVM), which is a variant of the SVM algorithm to classify the PolSAR image data. In order to take advantages of PolSAR data, five scattering features (entropy, total power, three Eigenvalues of Coherent Matrix: λ1,λ2,λ3) are input as original data space of the FSVM algorithm. The feasibility of this approach is examined by the JPL/AIRSAR PolSAR data. The classification results show that the proposed FSVM method has out-performed the SVM method.

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Duan, Hua, and Yan Mei Hou. "A Solving Algorithm of Fuzzy Support Vector Machines Based on Determination of Membership." Advanced Materials Research 756-759 (September 2013): 3399–403. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3399.

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In order to overcome the issues that Support Vector Machine is sensitive to the outlier and noise points, Fuzzy Support Vector Machine (FSVM) is proposed. The key issue to solve the FSVM is determinate the fuzzy membership. This paper gives an overview of construction algorithm of the fuzzy membership. We also give an algorithm to solve FSVM that is derived from improved-SMO algorithm.

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Yu, Lean. "Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/564213.

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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.

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Hou, Yuan Bin, Ning Li, Fan Guo, and Jing Chen. "Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector." Applied Mechanics and Materials 135-136 (October 2011): 547–52. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.547.

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Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, two kernel functions of FSVM is made for seeking the problem of random and nonlinearity, which are RBF and TANH. According to the random statistic data and the study sample, analyzing the effect of expense and kernel function in selecting different parameters, the unitary constant is ascertained, next, the FSVM kernel function of fault diagnosis multi-class rules are established, then, this method availability is proved using test data and simulation.

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Li, Kai, and Xiao Xia Lu. "A Rough Margin Based Fuzzy Support Vector Machine." Advanced Materials Research 204-210 (February 2011): 879–82. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.879.

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By combining fuzzy support vector machine with rough set, we propose a rough margin based fuzzy support vector machine (RFSVM). It inherits the characteristic of the FSVM method and considers position of training samples of the rough margin in order to reduce overfitting due to noises or outliers. The new proposed algorithm finds the optimal separating hyperplane that maximizes the rough margin containing lower margin and upper margin. Meanwhile, the points lied on the lower margin have larger penalty than these in the boundary of the rough margin. Experiments on several benchmark datasets show that the RFSVM algorithm is effective and feasible compared with the existing support vector machines.

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Gu, Xiaoqing, Tongguang Ni, and Hongyuan Wang. "New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/536434.

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In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.

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Chen, Hao Guang, Xiao Xi Li, and Da Xi Li. "New Fuzzy Support Vector Machine Method Based on Entropy and Ant-Colony Optimization." Applied Mechanics and Materials 380-384 (August 2013): 1580–84. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1580.

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Concerning the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current Fuzzy Support Vector Machines (FSVM), a new FSVM based on entropy and Genetic Algorithm (GA) named EGFSVM was proposed in this paper. Making use of evaluation of entropy and intelligence of GA, EGFSVM enhances the classification capability and makes clustering center more suitable and membership more accurate. Experimental results show EGFSVM has better precision and classification performance, especially to multi-class and large scale data.

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Khanali, Hoda, and Babak Vaziri. "Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data." Statistics, Optimization & Information Computing 9, no. 3 (July 10, 2021): 618–29. http://dx.doi.org/10.19139/soic-2310-5070-1035.

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Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed of Fuzzy C-means (FCM). So it reduces the sensitivity to noisy and outlier data, and enhances performance and quality of clusters. Since FVCM allocates some data to a specific cluster based on similarity technique, reducing the effect of noisy data increases the quality of the clusters. This paper presents a new approach to the accurate location of noisy data to the clusters overcoming the constraints of noisy points through fuzzy support vector machine (FSVM), called FVCM-FSVM, so that at each stage samples with a high degree of membership are selected for training in the classification of FSVM. Then, the labels of the remaining samples are predicted so the process continues until the convergence of the FVCM-FSVM. The results of the numerical experiments showed the proposed approach has better performance than FVCM. Of course, it greatly achieves high accuracy.

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Bing, Li, Liang Yilong, and Cheng Wei. "A fuzzy support vector machine based on environmental membership and its application to motor fault classification." Journal of Vibration and Control 24, no. 23 (March 19, 2018): 5681–92. http://dx.doi.org/10.1177/1077546318764484.

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To weaken the effects of the outliers or noise in classification, a fuzzy support vector machine (FSVM) based on environmental fuzzy membership is proposed. The environmental fuzzy membership considers not only the number of the similar samples nearby but also the distribution of the samples nearby. As more information of the samples is considered, the reliability and robustness of the FSVM is further enhanced, which can improve the classification performance, especially for overlapping samples. The classification performance of the proposed method is validated by numerical case studies, an experimental study for a breast cancer dataset, and an application to motor fault classification. Compared with the FSVM based on the k-nearest neighbor algorithm, the proposed method obtains more robust and accurate classification rates in all case studies.

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Zhang, Qing Xin, Yong Tao, and Zhan Bo Cui. "The Temperature Prediction in Blast Furnace Base on Fuzzy Least Squares Support Vector Machine." Applied Mechanics and Materials 336-338 (July 2013): 566–69. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.566.

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The temperature prediction in blast furnace loses accuracy or Forecasts failure when the temperatures change is at normal levels and obvious. This paper introduces fuzzy membership of samples basing on support vector data description and the fuzzy least squares support vector machine to forecast the blast furnace temperature. Then the simulation was done by using the forecast samples and the model after training by MATLAB. Comparing the simulation results of LS-FSVM with LS-SVM, the model basing on LS-FSVM enhances anti-jamming ability. The accuracy of the temperature prediction in blast furnace promotes significantly when the temperature of blast furnace fluctuates.

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Chaudhuri, Arindam. "Fuzzy Rough Support Vector Machine for Data Classification." International Journal of Fuzzy System Applications 5, no. 2 (April 2016): 26–53. http://dx.doi.org/10.4018/ijfsa.2016040103.

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In this paper, classification task is performed by FRSVM. It is variant of FSVM and MFSVM. Fuzzy rough set takes care of sensitiveness of noisy samples and handles impreciseness. The membership function is developed as function of cener and radius of each class in feature space. It plays an important role towards sampling the decision surface. The training samples are either linear or nonlinear separable. In nonlinear training samples, input space is mapped into high dimensional feature space to compute separating surface. The different input points make unique contributions to decision surface. The performance of the classifier is assessed in terms of the number of support vectors. The effect of variability in prediction and generalization of FRSVM is examined with respect to values of C. It effectively resolves imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. Experimental results on both synthetic and real datasets support that FRSVM achieves superior performance in reducing outliers' effects than existing SVMs.

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Zhang, Aihua, Yongchao Wang, Chen Chen, and Hamid Reza Karimi. "New Strategy for Analog Circuit Performance Evaluation under Disturbance and Fault Value." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/728201.

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Focus on this issue of disturbance and fault value is inevitable in data collection about analog circuit. A novel strategy is developed for analog circuit online performance evaluation based on fuzzy learning and double weighted support vector machine (DWMK-FSVM). First, the double weighted support vector regression machine is employed to be the indirect evaluation means, relied on the college analog electronic technology experiment to evaluate analog circuit. Second, the superiority of fuzzy learning also is addressed to realize active suppression to the fault values and disturbance parameters. Moreover, the multikernel RBF is employed by support vector regression machine to realize more flexibility online such as the bandwidths tuning. Numerical results, supported by the college analog circuit experiments, adopted OTL performance eight indexes, which were obtained via precision instrument evaluation in two years to construct training set and are then to be evaluated online based on DWMK-FSVM. Simulation results presented not only highlight precision of the evaluation strategy derived here but also illustrate its great robustness.

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Fei, Cheng-Wei, and Guang-Chen Bai. "Wavelet Correlation Feature Scale Entropy and Fuzzy Support Vector Machine Approach for Aeroengine Whole-Body Vibration Fault Diagnosis." Shock and Vibration 20, no. 2 (2013): 341–49. http://dx.doi.org/10.1155/2013/403920.

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In order to correctly analyze aeroengine whole-body vibration signals, Wavelet Correlation Feature Scale Entropy (WCFSE) and Fuzzy Support Vector Machine (FSVM) (WCFSE-FSVM) method was proposed by fusing the advantages of the WCFSE method and the FSVM method. The wavelet coefficients were known to be located in high Signal-to-Noise Ratio (S/N or SNR) scales and were obtained by the Wavelet Transform Correlation Filter Method (WTCFM). This method was applied to address the whole-body vibration signals. The WCFSE method was derived from the integration of the information entropy theory and WTCFM, and was applied to extract the WCFSE values of the vibration signals. Among the WCFSE values, theWFSE1andWCFSE2values on the scale 1 and 2 from the high band of vibration signal were believed to acceptably reflect the vibration feature and were selected to construct the eigenvectors of vibration signals as fault samples to establish the WCFSE-FSVM model. This model was applied to aeroengine whole-body vibration fault diagnosis. Through the diagnoses of four vibration fault modes and the comparison of the analysis results by four methods (SVM, FSVM, WESE-SVM, WCFSE-FSVM), it is shown that the WCFSE-FSVM method is characterized by higher learning ability, higher generalization ability and higher anti-noise ability than other methods in aeroengine whole-vibration fault analysis. Meanwhile, this present study provides a useful insight for the vibration fault diagnosis of complex machinery besides an aeroengine.

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CHUANG, LI-YEH, CHENG-HONG YANG, and LI-CHENG JIN. "CLASSIFICATION OF MULTIPLE CANCER TYPES USING FUZZY SUPPORT VECTOR MACHINES AND OUTLIER DETECTION METHODS." Biomedical Engineering: Applications, Basis and Communications 17, no. 06 (December 25, 2005): 300–308. http://dx.doi.org/10.4015/s1016237205000457.

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The support vector machine (SVM) is a new learning method and has shown comparable or better results than the neural networks on some applications. In this paper, we applied SVM to classify multiple cancer types by gene expression profiles and exploit some strategies of the SVM method, including fuzzy logic and statistical theories. Using the proposed strategies and outlier detection methods, the FSVM (fuzzy support vector machine) can achieve a comparable or better performance than other methods, and provide a more flexible architecture to discriminate against SRBCT and non-SRBCT samples.

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Mao, Yong, Xiaobo Zhou, Daoying Pi, Youxian Sun, and Stephen T. C. Wong. "Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 160–71. http://dx.doi.org/10.1155/jbb.2005.160.

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We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.

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Shan, Wei, Shensheng Cai, and Chen Liu. "A New Comprehensive Evaluation Method for Water Quality: Improved Fuzzy Support Vector Machine." Water 10, no. 10 (September 21, 2018): 1303. http://dx.doi.org/10.3390/w10101303.

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With the pressure of population growth and environmental pollution, it is particularly important to develop and utilize water resources more rationally, safely, and efficiently. Due to safety concerns, the government today adopts a pessimistic method, single factor assessment, for the evaluation of domestic water quality. At the same time, however, it is impossible to grasp the timely comprehensive pollution status of each area, so effective measures cannot be taken in time to reverse or at least alleviate its deterioration. Thus, the main propose of this paper is to establish a comprehensive evaluation model of water quality, which can provide the managers with timely information of water pollution in various regions. After considering various evaluation methods, this paper finally decided to use the fuzzy support vector machine method (FSVM) to establish the model that is mentioned above. The FSVM method is formed by applying the membership function to the support vector machine. However, the existing membership functions have some shortcomings, so after some improvements in these functions, a new membership function is finally formed. The model is then tested on the artificial data, UCI dataset, and water quality evaluation historical data. The results show that the improvement is meaningful, the improved fuzzy support vector machine has good performance, and it can deal with noise and outliers well. Thus, the model can completely solve the problem of comprehensive evaluation of water quality.

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Wei, Hai, Mingming Wang, Bingyue Song, Xin Wang, and Danlei Chen. "Study on the Magnitude of Reservoir-Triggered Earthquake Based on Support Vector Machines." Complexity 2018 (July 24, 2018): 1–10. http://dx.doi.org/10.1155/2018/2830690.

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An effective approach is introduced to predict the magnitude of reservoir-triggered earthquake (RTE), based on support vector machines (SVM) and fuzzy support vector machines (FSVM) methods. The main influence factors on RTE, including lithology, rock mass integrity, fault features, tectonic stress state, and seismic activity background in reservoir area, are categorized into 11 parameters and quantified by using analytical hierarchy process (AHP). Dataset on 100 reservoirs in China, including the 48 well-documented cases of RTE, are collected and used to train and validate the prediction models established with SVM and FSVM, respectively. Through numerical tests, it is found that both the SVM and FSVM models are effective in the prediction of the magnitude of RTE with high accuracy, provided that sufficient samples are collected. While the results of FSVM which is extended from SVM by introducing a fuzzy membership to reduce the influence of noises or outliers are found to be slightly less accurate than those of SVM in the current analysis of RTE cases. The reason might be attributed to the high discreteness of the sample data in the current study.

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Batuwita, Rukshan, and Vasile Palade. "FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning." IEEE Transactions on Fuzzy Systems 18, no. 3 (June 2010): 558–71. http://dx.doi.org/10.1109/tfuzz.2010.2042721.

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Younes, Dima, Essa Alghannam, Yuegang Tan, and Hong Lu. "Enhancement in Quality Estimation of Resistance Spot Welding Using Vision System and Fuzzy Support Vector Machine." Symmetry 12, no. 8 (August 18, 2020): 1380. http://dx.doi.org/10.3390/sym12081380.

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The current nondestructive testing methods such as ultrasonic, magnetic, or eddy current signals, and even the existing image processing methods, present certain challenges and show a lack of flexibility in building an effective and real-time quality estimation system of the resistance spot welding (RSW). This paper provides a significant improvement in the theory and practices for designing a robotized inspection station for RSW at the car manufacturing plants using image processing and fuzzy support vector machine (FSVM). The weld nuggets’ positions on each of the used car underbody models are detected mathematically. Then, to collect perfect pictures of the weld nuggets on each of these models, the required end-effector path is planned in real-time by establishing the Denavit-Hartenberg (D-H) model and solving the forward and inverse kinematics models of the used six-degrees of freedom (6-DOF) robotic arm. After that, the most frequent resistance spot-welding failure modes are reviewed. Improved image processing methods are employed to extract new features from the elliptical-shaped weld nugget’s surface and obtain a three-dimensional (3D) reconstruction model of the weld’s surface. The extracted artificial data of thousands of samples of the weld nuggets are divided into three groups. Then, the FSVM learning algorithm is formed by applying the fuzzy membership functions to each group. The improved image processing with the proposed FSVM method shows good performance in classifying the failure modes and dealing with the image noise. The experimental results show that the improvement of comprehensive automatic real-time quality evaluation of RSW surfaces is meaningful: the quality estimation could be processed within 0.5 s in very high accuracy.

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Chen, Yi, Yu Hui Li, Fan Zhang, and Feng Zhou. "Vehicle Classification Algorithm Based on Fuzzy SVM Models." Applied Mechanics and Materials 444-445 (October 2013): 841–48. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.841.

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As a typical binary classifier, its an inseparable sample problem about the Support Vector Machine (SVM) when processing the classification of the multi-class vehicle models. Since the SVM can not estimate the effect size of the samples classification accurately, and then reduces the classification generalization ability. In this paper, a fuzzy Support Vector Machine (FSVM) classification algorithm is applied to vehicle classification. According to the difference of the contribution which the vehicle characteristics make to the classification, the appropriate degree of membership is given, and the algorithm improves the vehicle models classification ability of the traditional SVM effectively. The experimental results show that the new method, compared with the existing vehicle classification method, is feasible, effective, and with a high classification accuracy

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Punniyamoorthy, M., and P. Sridevi. "Identification of a standard AI based technique for credit risk analysis." Benchmarking: An International Journal 23, no. 5 (July 4, 2016): 1381–90. http://dx.doi.org/10.1108/bij-09-2014-0094.

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Purpose – Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating agencies to predict the ability of creditors to meet financial persuasions. The purpose of this paper is to construct neural network (NN) and fuzzy support vector machine (FSVM) classifiers to discriminate good creditors from bad ones and identify a best classifier for credit risk assessment. Design/methodology/approach – This study uses artificial neural network, the most popular AI technique used in the field of financial applications for classification and prediction and the new machine learning classification algorithm, FSVM to differentiate good creditors from bad. As membership value on data points influence the classification problem, this paper presents the new FSVM model. The instances membership is computed using fuzzy c-means by evolving a new membership. The FSVM model is also tested on different kernels and compared and the classifier with highest classification accuracy for a kernel is identified. Findings – The paper identifies a standard AI model by comparing the performances of the NN model and FSVM model for a credit risk data set. This work proves that that FSVM model performs better than back propagation-neural network. Practical implications – The proposed model can be used by financial institutions to accurately assess the credit risk pattern of customers and make better decisions. Originality/value – This paper has developed a new membership for data points and has proposed a new FCM-based FSVM model for more accurate predictions.

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Lee, Hyun-Jik, Yoon-Ho Kim, and Joo-Shin Lee. "Age of Face Classification based on Gabor Feature and Fuzzy Support Vector Machines." Journal of Korea Navigation Institute 16, no. 1 (February 29, 2012): 151–57. http://dx.doi.org/10.12673/jkoni.2012.16.1.151.

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Zhou Muchun, 周木春, 赵. 琦. Zhao Qi, 陈延如 Chen Yanru, and 邵艳明 Shao Yanming. "State recognition of light radiation of BOF end-point based on fuzzy support vector machine." Infrared and Laser Engineering 47, no. 7 (2018): 726004. http://dx.doi.org/10.3788/irla201847.0726004.

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Jebamony, Jebasonia, and Dheeba Jacob. "Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 6 (July 27, 2020): 703–10. http://dx.doi.org/10.2174/1573405615666190801121506.

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Background: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. Objective: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. Methods: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Conclusion: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

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Roy, Amarjit, Joyeeta Singha, and Rabul Hussain Laskar. "Removal of Impulse Noise from Gray Images Using Fuzzy SVM Based Histogram Fuzzy Filter." Journal of Circuits, Systems and Computers 27, no. 09 (April 26, 2018): 1850139. http://dx.doi.org/10.1142/s0218126618501396.

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Impulse noise is an image noise that degrades the quality of the image drastically. In this paper, k-means clustering has been incorporated with fuzzy-support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from gray images. Here, local binary pattern (LBP) has been incorporated with previously used feature vector prediction error of the processing pixel, absolute difference between median value and processing pixel, median pixel, pixel under operation and mean value around the processing kernel. In this work, [Formula: see text]-means clustering has been used for reducing the feature vector set, where features have been extracted from the images corrupted with 10%, 50%, and 90% impulse noise. If the pixel is depicted as noisy in testing phase, histogram adaptive fuzzy filter is processed over the noisy pixel under operation. It is seen that the proposed filter offers improved performance over some of the state-of-the-art filter in terms of different image quality measures likely PSNR, SSIM, MSE, FSIM, etc. It is observed that performance is increased by [Formula: see text][Formula: see text]2–5[Formula: see text]dB than baseline filters likely SVM fuzzy filter, and artificial neural network based adaptive sized mean filter (ANNASMF) especially at high density noise.

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Han, Hui, and Lina Hao. "Fault diagnosis method of rolling bearings Based on SPA-FE-IFSVM." Advances in Mechanical Engineering 12, no. 10 (October 2020): 168781402096947. http://dx.doi.org/10.1177/1687814020969470.

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Rolling bearings are the most frequently failed components in rotating machinery. Once a failure occurs, the entire system will be shut down or even cause catastrophic consequences. Therefore, a fault detection of rolling bearings is of great significance. Due to the complexity of the mechanical system, the randomness of the vibration signal appears on different scales. Based on the multi-scale fuzzy entropy (FE) analysis of the vibration signal, a rolling bearing fault diagnosis method based on smoothness priors approach (SPA) -FE-IFSVM is proposed. The SPA method was used to adaptively decompose the vibration signal and obtain the trend item and de-trend item of the vibration signal. Then the fuzzy entropy of the trend item and de-trend item was calculated respectively. Meanwhile, aiming at the problem that the support vector machine (SVM) cannot process the data set containing fuzzy messages and was sensitive to noise, the fuzzy support vector machine (FSVM) was introduced and improved, and then the FE as the feature vector was input into the improved fuzzy support vector machine (IFSVM) to identify the failure. The method was applied to the rolling bearing experimental data. The analysis results show that: this method can achieve 100% fault diagnosis accuracy when only two component features are extracted, which can effectively realize the fault diagnosis of rolling bearings.

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Ding, Yan, Xuemei Chen, Shan Zhong, and Li Liu. "Emotion Analysis of College Students Using a Fuzzy Support Vector Machine." Mathematical Problems in Engineering 2020 (September 10, 2020): 1–11. http://dx.doi.org/10.1155/2020/8931486.

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With the rapid development of society, the number of college students in our country is on the rise. College students are under pressure due to challenges from the society, school, and family, but they cannot find a suitable solution. As a result, the psychological problems of college students are diversified and complicated. The mental health problem of college students is becoming more and more serious, which requires urgent attention. This article realizes the monitoring of university mental health by identifying and analyzing the emotions of college students. This article uses EEG to determine the emotional state of college students. First, feature extraction is performed on different rhythm data of EEG, and then a fuzzy support vector machine (FSVM) is used for classification. Finally, a decision fusion mechanism based on the D-S evidence combination theory is used to fuse the classification results and output the final emotion recognition results. The contribution of this research is mainly in three aspects. One is the use of multiple features, which improves the efficiency of data use; the other is the use of a fuzzy support vector machine classifier with higher noise resistance, and the recognition rate of the model is better. The third is that the decision fusion mechanism based on the D-S evidence combination theory takes into account the classification results of each feature, and the classification results assist each other and integrate organically. The experiment compares emotion recognition based on single rhythm, multirhythm combination, and multirhythm fusion. The experimental results fully prove that the proposed emotion recognition method can effectively improve the recognition efficiency. It has a good practical value in the emotion recognition of college students.

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Chen, Zai Ping, and Jun Lv. "Prediction of Tank Bottom Corrosion Classification Based on FSVM." Applied Mechanics and Materials 441 (December 2013): 590–93. http://dx.doi.org/10.4028/www.scientific.net/amm.441.590.

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The condition of tank bottom corrosion is the determinant factor affecting the safe operation of the tank. Considering the difficulty to shut down and carry out testing on tank floors, This article determines the characteristic parameters of tank bottom corrosion based on tank bottom magnetic flux leakage testing results and the fuzzy support vector machine is established based on the characteristic parameters of tank bottom corrosion. Through the prediction and analysis on 25 tanks in a depot, comparing with the result of magnetic flux leakage test, coincidence rate reaches 100%.The results show that this prediction technique is able to resolve the problem of the prediction of tank bottom corrosion rank, and can be applied in engineering applications.

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Liu, Xiaobin, Xiran Zhang, Xiaoyi Guo, Yijie Ding, Weiwei Shan, Liang Wang, Wei Zhou, and Hua Shi. "A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients." Computational and Mathematical Methods in Medicine 2021 (July 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/2464821.

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In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascular calcification risk in ESRD patients. As the amount of collected data is unbalanced under different risk levels, it has an influence on the classification task. So, an effective fuzzy support vector machine based on self-representation (FSVM-SR) is proposed to predict vascular calcification risk in this work. In addition, our method is also compared with other conventional machine learning methods, and the results show that our method can better complete the classification task of the vascular calcification risk.

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Yuan, Yong-bin, Sheng Lan, Xu Yu, and Miao Yu. "Algorithm of Fuzzy Support Vector Machine based on a Piecewise Linear Fuzzy Weight Method." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 2 (April 2018): 62–76. http://dx.doi.org/10.4018/ijcini.2018040105.

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This article describes how fuzzy support vector machines (FSVMs) function well with good anti-noise performance, which receives the attention of many experts. However, the traditional center-distance fuzzy weight assignment method assigns support vectors with a small value of a membership degree and this weakens the role of support vectors in classification. In this article, a piecewise linear fuzzy weight computing method is proposed, in which boundary samples are assigned with a larger value of membership degree and samples far from the mean vector are assigned a smaller value of membership degree. The proposed method has a good classification performance, because the influence of noise samples is weakened and meanwhile the support vectors are paid much more attention. The experiments on the UCI database and MNIST data set fully verify the effectiveness of the proposed algorithm.

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Wang, Peng, and Zhengliang Xu. "A Novel Consumer Purchase Behavior Recognition Method Using Ensemble Learning Algorithm." Mathematical Problems in Engineering 2020 (December 19, 2020): 1–10. http://dx.doi.org/10.1155/2020/6673535.

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With the prosperous development of e-commerce platforms, consumer returns often occur. The issue of returns has become a stumbling block to the profitability of e-commerce companies. To protect consumers’ purchase rights, the Chinese government has introduced a 7-day unreasonable return policy. In order to use the return policy to attract consumers to buy, various e-commerce platforms have created a more relaxed and convenient return environment for consumers. On the one hand, the introduction of the return policy has increased customer trust in e-commerce platforms and stimulated purchase demand. On the other hand, the return behavior also increases the cost of the e-commerce platform. With the upgrading of consumption, customers pay more attention to personalized experience. In addition to considering price when purchasing online, the quality of services provided by e-commerce platforms will also directly affect customers’ purchasing decisions and return behavior. Therefore, under the personalized return policy of the e-commerce platform, whether consumers will make another purchase is worth studying. In order to achieve this goal, an ensemble learning method (AdaBoost-FSVM) based on fuzzy support vector machine (FSVM) is applied to predict the purchase intention of consumers. First, the grid search method is used to optimize the modeling parameters of the FSVM base classifier. Second, the AdaBoost-FSVM ensemble prediction model is constructed by using multiple base classifiers. In order to evaluate the performance of the prediction models used, logistic regression (LR), support vector machine (SVM), FSVM, random forest (RF), and XGBoost were used to construct prediction models for purchasing behavior. The experimental results demonstrate that the method used in this study has a more accurate prediction effect than the comparison algorithms. The predictive model used in this study can be used in the recommendation system of shopping websites and can also be used to guide e-commerce companies to customize various preferential policies and services, so as to quickly and accurately stimulate the purchase intention of more potential consumers.

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Qin, Chuandong, and Huixia Zhao. "Selecting the Optimal Combination Model of FSSVM for the Imbalance Datasets." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/539430.

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Imbalanced data learning is one of the most active and important fields in machine learning research. The existing class imbalance learning methods can make Support Vector Machines (SVMs) less sensitive to class imbalance; they still suffer from the disturbance of outliers and noise present in the datasets. A kind of Fuzzy Smooth Support Vector Machines (FSSVMs) are proposed based on the Smooth Support Vector Machine (SSVM) of O. L. Mangasarian. SSVM can be computed by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm or the Newton-Armijo algorithm easily. Two kinds of fuzzy memberships and three smooth functions can be chosen in the algorithms. The fuzzy memberships consider the contribution rate of each sample to the optimal separating hyperplane. The polynomial smooth functions can make the optimization problem more accurate at the inflection point. Those changes play the active effects on trials. The results of the experiments show that the FSSVMs can gain the better accuracy and the shorter time than the SSVMs and some of the other methods.

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Chen, Jiajia, Wuhua Jiang, Pan Zhao, and Jinfang Hu. "A path planning method of anti-jamming ability improvement for autonomous vehicle navigating in off-road environments." Industrial Robot: An International Journal 44, no. 4 (June 19, 2017): 406–15. http://dx.doi.org/10.1108/ir-11-2016-0301.

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Purpose Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments. Design/methodology/approach The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm. Findings The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance. Originality/value This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

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Fauzi, I. R., Z. Rustam, and A. Wibowo. "Multiclass classification of leukemia cancer data using Fuzzy Support Vector Machine (FSVM) with feature selection using Principal Component Analysis (PCA)." Journal of Physics: Conference Series 1725 (January 2021): 012012. http://dx.doi.org/10.1088/1742-6596/1725/1/012012.

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Sun, Lin, Jiucheng Xu, Xingxing Zhang, and Yun Tian. "An Image Watermarking Scheme Using Arnold Transform and Fuzzy Smooth Support Vector Machine." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/931672.

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With the development of information security, the traditional encryption algorithm for image has been far from ensuring the security of image in the transmission. This paper presents a new image watermarking scheme based on Arnold Transform (AT) and Fuzzy Smooth Support Vector Machine (FSSVM). First of all, improved AT (IAT) is obtained by adding variables and expanding transformation space, and FSSVM is proposed by introducing fuzzy membership degree. The embedding positions of watermark are obtained from IAT, and the pixel values are embedded in carrier image by quantization embedding rules. Then, the watermark can be embedded in carrier image. In order to realize blind extraction of watermark, FSSVM model is used to find the embedding positions of watermark, and the pixel values are extracted by using quantization extraction rules. Through using improved Arnold inverse transformation for embedding positions, the watermark coordinates can be calculated, and the extraction of watermark is carried out. Compared with other watermarking techniques, the presented scheme can promote the security by adding more secret keys, and the imperceptibility of watermark is improved by introducing quantization rules. The experimental results show that the proposed method outperforms many existing methods against various types of attacks.

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Sun, R.-R., and Y.-Y. Wang. "Predicting spontaneous termination of atrial fibrillation based on the RR interval." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 223, no. 6 (July 9, 2009): 713–26. http://dx.doi.org/10.1243/09544119jeim576.

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It is important to characterize conditions under which atrial fibrillation (AF) is likely to terminate spontaneously. A novel method is proposed here. Eleven features are first extracted to characterize RR interval and Poincaré plot from a statistical viewpoint and a geometric viewpoint respectively. Then sequential forward search (SFS) algorithm is utilized for feature selection. Finally, a fuzzy support vector machine (FSVM) with a new fuzzy membership is applied for AF termination prediction. The method is studied with an AF database of electrocardiogram (ECG) recordings provided by PhysioNet for the Cardiology Challenge 2004. It is divided into a training set and two testing sets (A and B). Experiment results show that 100 per cent of testing set A and 100 per cent of testing set B are correctly classified, together with 92.3 per cent of non-terminating and soon-terminating AF correctly classified. It demonstrates that the proposed method can predict spontaneous termination of AF effectively.

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Liu, Yi Yan, Shuan Hai He, Yong Feng Ju, and Chen Dong Duan. "Transformer Fault Diagnosis Based on Fuzzy Support Vector Machines." Applied Mechanics and Materials 135-136 (October 2011): 1102–7. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.1102.

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Due to lack of typical damage samples in the transformer fault diagnosis, a new fault diagnosis method based on fuzzy support vector machines (FFSVMs) was presented. According to the method, the five characteristic gases dissolved in transformer oil were extracted by the K-means clustering (KMC) method as feature vectors, which were input to fuzzy optimal multi-classified SVMs for training. Then the FSVMs diagnosis model was established to implement fault samples classification. Experiment showed that by adopting facture extraction with KMC, the diagnosis information was concentrated and the consuming in parameter determination was solved effectively. The presented method enabled to detect transformer faults with a high correct judgment rate, and can be used as an automation approach for diagnosis under condition of small samples.

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Ji, Ai-bing, Songcan Chen, and Qiang Hua. "Fuzzy classifier based on fuzzy support vector machine." Journal of Intelligent & Fuzzy Systems 26, no. 1 (2014): 421–30. http://dx.doi.org/10.3233/ifs-130819.

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Cai, Ai Ping. "An Algorithm of Edge Detection Based on FSVM." Applied Mechanics and Materials 321-324 (June 2013): 1046–50. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1046.

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The support vector machine (SVM) has been shown to be an efficient approach for a variety of classification problems. It has also been widely used in target identification and tracking, motion analysis, image segmentation technology. Traditional detection methods mostly exist pseudo-edge and poor anti-noise capability. Under these circumstances, developing an efficient method is necessary. In this paper, we propose a new detection algorithm based on FSVM, the main idea is to train classified sample and give all training data a degree of membership, increase punishment to the wrong sub-sample. Then training and testing the FSVM classification model. Finally, extract edge of the image by using FSVM classification model. Experimental results show that the new algorithm can detect a clear image edge and have a good anti-noise nature.

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Ju, Hongmei, Qiuling Hou, and Ling Jing. "Fuzzy and interval-valued fuzzy nonparallel support vector machine." Journal of Intelligent & Fuzzy Systems 36, no. 3 (March 26, 2019): 2677–90. http://dx.doi.org/10.3233/jifs-18702.

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Forghani, Yahya, and Hadi Sadoghi Yazdi. "Robust support vector machine-trained fuzzy system." Neural Networks 50 (February 2014): 154–65. http://dx.doi.org/10.1016/j.neunet.2013.11.013.

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Li, Kai, and Jie Li. "Structural Fuzzy Multi-class Support Vector Machine." Journal of Physics: Conference Series 1631 (September 2020): 012188. http://dx.doi.org/10.1088/1742-6596/1631/1/012188.

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Chaudhuri, Arindam, and Kajal De. "Fuzzy Support Vector Machine for bankruptcy prediction." Applied Soft Computing 11, no. 2 (March 2011): 2472–86. http://dx.doi.org/10.1016/j.asoc.2010.10.003.

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Ju, Hongmei, Ye Zhao, and Yafang Zhang. "Directed acyclic graph fuzzy nonparallel support vector machine." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 1457–70. http://dx.doi.org/10.3233/jifs-201847.

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Classification problem is an important research direction in machine learning. Nonparallel support vector machine (NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. When solving multi-class classification problems, NPSVM will encounter the problem of sample noises, low discrimination speed and unrecognized regions, which will affect its performance. In this paper, based on the multi-class NPSVM model, two improvements are made, and a directed acyclic graph fuzzy nonparallel support vector machine (DAG-F-NPSVM) model is established. On the one hand, for the noises that may exist in the data set, the density information is used to add fuzzy membership to the samples, so that the contribution of each samples to the classification is treated differently. On the other hand, in order to reduce the decision time and solve the problem of unrecognized regions, the theory of directed acyclic graph (DAG) is introduced. Finally, the advantages of the new model in classification accuracy and decision speed is verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of this new method.

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Wang, Tinghua, Yunzhi Qiu, and Jialin Hua. "Centered kernel alignment inspired fuzzy support vector machine." Fuzzy Sets and Systems 394 (September 2020): 110–23. http://dx.doi.org/10.1016/j.fss.2019.09.017.

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Huang, Xi-xia, Fan-huai Shi, Wei Gu, and Shan-ben Chen. "Support vector machine-based fuzzy rules acquisition system." Journal of Shanghai Jiaotong University (Science) 14, no. 5 (October 2009): 555–61. http://dx.doi.org/10.1007/s12204-009-0555-8.

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Ju, Hongmei, Yafang Zhang, and Ye Zhao. "υ-Nonparallel parametric margin fuzzy support vector machine." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11731–47. http://dx.doi.org/10.3233/jifs-202869.

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Classification problem is an important research direction in machine learning. υ-nonparallel support vector machine (υ-NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. However, when solving classification problems, υ-NPSVM will encounter the problem of sample noises and heteroscedastic noise structure, which will affect its performance. In this paper, two improvements are made on the υ-NPSVM model, and a υ-nonparallel parametric margin fuzzy support vector machine (par-υ-FNPSVM) is established. On the one hand, for the noises that may exist in the data set, the neighbor information is used to add fuzzy membership to the samples, so that the contribution of each sample to the classification is treated differently. On the other hand, in order to reduce the effect of heteroscedastic structure, an insensitive loss function is introduced. The advantages of the new model are verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of it.

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Journal articles on the topic 'Fuzzy Support Vector Machine (FSVM)'

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