The Research on Semi-Supervised Support Vector Data Description Multi-Classification Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1115-1120
Author(s):  
De Qian Xue
Keyword(s):  
Learning Difficulties ◽  
Classification Problem ◽  
Classification Algorithm ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Degree Of Membership ◽  
Simulation Results ◽  
Multi Classification

Semi-supervised Support Vector Data Description multi-classification algorithm is presented, in order to solve less labeled data learning, difficulties in the implementation and poor results of semi-supervised multi-classification, which full use the distribution of information in of non-target samples. S3VDD-MC algorithm defines the degree of membership of non-target samples, in order to get the non-target samples’ accepted labels or refused labels, on this basis, several super-spheres constructed, a k-classification problem is transformed into k SVDDs problem. Finally, the simulation results verify the effectiveness of the algorithm.

The Temperature Prediction in Blast Furnace Base on Fuzzy Least Squares Support Vector Machine

2013 ◽  
Vol 336-338 ◽  
pp. 566-569
Author(s):  
Qing Xin Zhang ◽  
Yong Tao ◽  
Zhan Bo Cui
Keyword(s):  
Support Vector Machine ◽  
Blast Furnace ◽  
Least Squares ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Furnace Temperature ◽  
Temperature Prediction ◽  
Data Description ◽  
Simulation Results

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.

scholarly journals Monitoring the mean with least-squares support vector data description

2021 ◽  
Vol 28 (3) ◽  
Author(s):  
Edgard M. Maboudou-Tchao
Keyword(s):  
Least Squares ◽  
Statistical Process Control ◽  
Control Charts ◽  
Classification Problem ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Statistical Process ◽  
Data Description ◽  
The Mean

Abstract: Multivariate control charts are essential tools in multivariate statistical process control (MSPC). “Shewhart-type” charts are control charts using rational subgroupings which are effective in the detection of large shifts. Recently, the one-class classification problem has attracted a lot of interest. Three methods are typically used to solve this type of classification problem. These methods include the k−center method, the nearest neighbor method, one-class support vector machine (OCSVM), and the support vector data description (SVDD). In industrial applications, like statistical process control (SPC), practitioners successfully used SVDD to detect anomalies or outliers in the process. In this paper, we reformulate the standard support vector data description and derive a least squares version of the method. This least-squares support vector data description (LS-SVDD) is used to design a control chart for monitoring the mean vector of processes. We compare the performance of the LS-SVDD chart with the SVDD and T2 chart using out-of-control Average Run Length (ARL) as the performance metric. The experimental results indicate that the proposed control chart has very good performance.

scholarly journals A Support Vector Data Description Approach to NLOS Identification in UWB Positioning

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shiwei Tian ◽  
Luwen Zhao ◽  
Guangxia Li
Keyword(s):  
Identification Problem ◽  
Classification Problem ◽  
Line Of Sight ◽  
Support Vector ◽  
Delay Spread ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
One Class Classification ◽  
Mean Excess Delay

Non-line-of-sight (NLOS) propagation is one of the most important challenges in radio positioning, and, in recent years, significant attention has been drawn to the identification and mitigation of NLOS signals. This paper focuses on the identification of NLOS signals. The authors consider the NLOS identification problem as a one-class classification problem and apply the support vector data description (SVDD), providing accurate data descriptions utilizing kernel techniques, to perform NLOS identification in ultrawide bandwidth (UWB) positioning. Our work is based on the fact that some features extracted from the received signal waveforms, such as the kurtosis, the mean excess delay spread, and the root mean square delay spread, are different between line-of-sight (LOS) and NLOS signals. Numerical simulations are performed to demonstrate the performance, using a dataset derived from a measurement campaign.

EL-SVDD: An Improved and Localized Multi-Class Classification Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1693-1698 ◽  
Author(s):  
Jian Xi Peng ◽  
Jian Xiong Tan
Keyword(s):  
Classification Algorithm ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Accuracy Analysis ◽  
Vector Data ◽  
Data Description ◽  
Multi Class Classification ◽  
Parameter Values ◽  
Mutual Communication

In order to solve deviation and imbalance of the traditional multi-class classification. This paper designs an improved localized multi-class classification algorithm based on mutual communication entropy and Support Vector Data Description (SVDD), know as EL-SVDD algorithm. First, this algorithm calculates parameter values of the mutual communication entropy with many local classes of samples. Second, one class is placed inside the multidimensional sphere based on the mutual communication entropy. Finally, according to the samples and parameter values of the mutual communication entropy, it reinterpreted the C values of SVDD algorithm. As the result, the experiments shows that EL-SVDD algorithm not only has the feasibility, but also can improve the accuracy analysis of multi-class classification stably and effectively.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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