A Multi-Classification Algorithm of Semi-Supervised Support Vector Data Description Based on Pairwise Constraints

2013 ◽  
pp. 531-538 ◽  
Author(s):  
Ying Zhao ◽  
Guan-jun Wang
Keyword(s):  
Classification Algorithm ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Pairwise Constraints ◽  
Data Description ◽  
Multi Classification

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.

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.

Bridge health anomaly detection using deep support vector data description

2021 ◽  
Author(s):  
JianXi Yang ◽  
Fei Yang ◽  
Likai Zhang ◽  
Ren Li ◽  
Shixin Jiang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Deep Support
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