A modified support vector data description based novelty detection approach for machinery components

2013 ◽  
Vol 13 (2) ◽  
pp. 1193-1205 ◽  
Author(s):  
Shijin Wang ◽  
Jianbo Yu ◽  
Edzel Lapira ◽  
Jay Lee
Keyword(s):  
Novelty Detection ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Detection Approach

scholarly journals Using a dynamically selective support vector data description model to discover novelties in the control system of electric arc furnace

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1049-1058
Author(s):  
Jiong Zhang ◽  
Yue Wang ◽  
Qian Li ◽  
Biao Wang
Keyword(s):  
Novelty Detection ◽  
Probabilistic Method ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Arc Furnace ◽  
Vector Data ◽  
Dynamic Selection ◽  
Data Description

As increasing data-driven control strategies are applied in electric arc furnace systems, the problem of novelty detection has drawn more attentions than before. The presence of outliers should be the main obstacle in practical applications for these advanced control techniques. To this end, this paper proposes a dynamically selective support vector data description model to discover novelties in electric arc furnace. In this model, support vector data description plays the role of base detector. Artificial outliers are generated with two objectives, one is to assist the dynamic selection, and the other is to optimize two parameters of support vector data description. Then clustering technique is used to determine the validation set for each test point. Finally, a probabilistic method is used to compute the competence of base detectors. In contrast to other novelty ensembles that have parallel structures, our ensemble model has a dynamic selection mechanism that could facilitate the mining of the potential of base detectors. Three synthetic and three real-world datasets are used to validate the effectiveness of the proposed detection model. Experimental results have approved our method by comparing it with several competitors.

Privacy preserving and fast decision for novelty detection using support vector data description

2014 ◽  
Vol 19 (5) ◽  
pp. 1171-1186 ◽  
Author(s):  
Wenjun Hu ◽  
Shitong Wang ◽  
Fu-lai Chung ◽  
Yong Liu ◽  
Wenhao Ying
Keyword(s):  
Novelty Detection ◽  
Privacy Preserving ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Fast Decision

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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