scholarly journals Techniques for Selecting the Optimal Parameters of One-Class Support Vector Machine Classifier for Reduced Samples

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Optimal Parameters ◽  
Target Class ◽  
Large Dataset ◽  
Training Samples ◽  
Reduced Parameters ◽  
Selection Parameter ◽  
The One ◽  
Effective Use

Usually, the One-Class Support Vector Machine (OC-SVM) requires a large dataset for modeling effectively the target class independently to other classes. For finding the OC-SVM model, the available dataset is subdivided into two subsets namely training and validation, which are used for training and validating the optimal parameters. This approach is effective when a large dataset is available. However, when training samples are reduced, parameters of the OC-SVM are difficult to find in absence of the validation subset. Hence, this paper proposes various techniques for selecting the optimal parameters using only a training subset. The experimental evaluation conducted on several real-world benchmarks proves the effective use of the new selection parameter techniques for validating the model of OC-SVM classifiers versus the standard validation techniques

scholarly journals A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification

2019 ◽  
Vol 13 (3) ◽  
pp. 219 ◽  
Author(s):  
Styawati Styawati ◽  
Khabib Mustofa
Keyword(s):  
Support Vector Machine ◽  
Parameter Optimization ◽  
Firefly Algorithm ◽  
Optimization Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Test Results ◽  
Optimal Parameters ◽  
The One

The sentiment analysis used in this study is the process of classifying text into two classes, namely negative and positive classes. The classification method used is Support Vector Machine (SVM). The successful classification of the SVM method depends on the soft margin coefficient C, as well as the σ parameter of the kernel function. Therefore we need a combination of SVM parameters that are appropriate for classifying film opinion data using the SVM method. This study uses the Firefly method as an SVM parameter optimization method. The dataset used in this study is public opinion data on several films. The results of this study indicate that the Firefly Algorithm (FA) can be used to find optimal parameters in the SVM classifier. This is evidenced by the results of SVM system testing using 2179 data with nine SVM parameter combinations resulting in 85% highest accuracy, while the FA-SVM system with nine population and generation combinations produces the highest accuracy of 88%. The second test results using 1200 data using the same combination as the one test, the SVM method produces the highest accuracy of 87%, while the FA-SVM method produces the highest accuracy of 89%.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

Membership Calculation Based on Dimension Hierarchical Division

2013 ◽  
Vol 475-476 ◽  
pp. 312-317
Author(s):  
Ping Zhou ◽  
Jin Lei Wang ◽  
Xian Kai Chen ◽  
Guan Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Bottom Up ◽  
Generalization Ability ◽  
Clustering Technique ◽  
Novel Approach ◽  
Training Samples ◽  
Noise Sample

Since dataset usually contain noises, it is very helpful to find out and remove the noise in a preprocessing step. Fuzzy membership can measure a samples weight. The weight should be smaller for noise sample but bigger for important sample. Therefore, appropriate sample memberships are vital. The article proposed a novel approach, Membership Calculate based on Hierarchical Division (MCHD), to calculate the membership of training samples. MCHD uses the conception of dimension similarity, which develop a bottom-up clustering technique to calculate the sample membership iteratively. The experiment indicates that MCHD can effectively detect noise and removes them from the dataset. Fuzzy support vector machine based on MCHD outperforms most of approaches published recently and hold the better generalization ability to handle the noise.

scholarly journals Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 152 ◽  
Author(s):  
Su-qi Zhang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Local Optimum ◽  
Short Term ◽  
Swarm Optimization ◽  
Traffic Flow Forecasting ◽  
Learning Machine ◽  
The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

SVM-Based Segmentation-Verification of Handwritten Connected Digits Using the Oriented Sliding Window

2015 ◽  
Vol 14 (01) ◽  
pp. 1550005 ◽  
Author(s):  
Abdeljalil Gattal ◽  
Youcef Chibani
Keyword(s):  
Support Vector Machine ◽  
Sliding Window ◽  
Experimental Results ◽  
Support Vector ◽  
Decision Module ◽  
Handwritten Digit ◽  
Effective Use ◽  
Cutting Path

We propose in this paper a system to recognize handwritten digit strings, which constitutes a difficult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-verification of handwritten connected digits based conjointly on the oriented sliding window and support vector machine (SVM) classifiers. The proposed approach allows separating adjacent digits according the connection configuration by finding at the same time the interconnection points between adjacent digits and the cutting path. SVM-based segmentation-verification using the global decision module allows the rejection or acceptance of the processed image. Experimental results conducted on a large synthetic database of handwritten digits show the effective use of the oriented sliding window for segmentation-verification.

scholarly journals Through-Wall Detection with LS-SVM under Unknown Wall Characteristics

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Fangfang Wang ◽  
Yerong Zhang ◽  
Huamei Zhang
Keyword(s):  
Support Vector Machine ◽  
Signal To Noise Ratio ◽  
Support Vector ◽  
Detection Accuracy ◽  
Step Procedure ◽  
Training Samples ◽  
Scattered Fields ◽  
Value Decomposition ◽  
The Relationship ◽  
Wall Imaging

One of the main challenges in through-wall imaging (TWI) is the presence of the walls, whose returns tend to obscure the target behind the walls and must be considered and computed in the imaging procedure. In this paper, a two-step procedure for the through-wall detection is proposed. Firstly, an effective clutter mitigation method based on singular value decomposition (SVD) is used. It does not require knowledge of the background scene or rely on accurate modeling and estimation of wall parameters. Then, TWI problem is cast as a regression one and solved by means of least-squares support vector machine (LS-SVM). The complex scattering process due to the presence of the walls is automatically included in the nonlinear relationship between the feature vector extracted from the target scattered fields and the position of the target. The relationship is obtained through a training phase using LS-SVM. Simulated results show that the proposed approach is effective. We also analyze the impacts of training samples and signal-to-noise ratio (SNR) on test detection accuracy. Simulated results reveal that the proposed LS-SVM based approach can provide comparative performances in terms of accuracy, convergence, robustness, and generalization in comparison with the support vector machine (SVM) based approach.

Predicting the Characteristics of Biofouling Mass Based on Support Vector Machine

2013 ◽  
Vol 459 ◽  
pp. 60-64
Author(s):  
Xiao Qiang Wen
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Ethylene Terephthalate ◽  
Support Vector ◽  
Average Amount ◽  
Poly Ethylene Terephthalate ◽  
Training Samples ◽  
Svm Model ◽  
Chemical Character ◽  
Poly Ethylene

A new prediction model of material chemical character effects on biofouling mass was built based on Support Vector Machine (SVM), in which there were four input vectors, which were carbon content, hydrogen content and oxygen content of the solid materials and flow rate, and one output vectors, which was the average amount of biofouling formed on the solid surface. Firstly, creating the sample database and normalizing all samples. Secondly, training the model based on the training samples to obtain the optimal prediction model, then, predicting the training samples. Comparing with experimental results, the accuracy of the SVM model is 95.5%. Besides, the model was tested by poly (ethylene terephthalate), and the predicted and actual results are consistent. Thus, the construction of the predictive model is reasonable and feasible.

scholarly journals Scene Oriented Classification of Blurry and Noisy Images Using SVM with Fuzzy C Mean Clustering

2014 ◽  
Vol 12 (4) ◽  
pp. 3393-3402
Author(s):  
Deepak Nema
Keyword(s):  
Support Vector Machine ◽  
Hierarchical Clustering ◽  
Hierarchical Classification ◽  
Computation Time ◽  
Support Vector ◽  
Self Organizing Map ◽  
Large Dataset ◽  
Noisy Images ◽  
Self Organizing

Image classification is a challenging task in image processing especially in the case of blurry and noisy images. In this work, we present an extension of scene oriented hierarchical classification of blurry and noisy images using Support Vector Machine (SVM) and Fuzzy C-Mean. Generally, a system for scene-oriented classification of blurry and noisy images attempts to simulate major features of the human visual observation. These approaches are  based on three strategies such as Global pathway for extracting essential signature of image, local pathway for extracting local features, and then outcome of both global and local phase are combined and define feature vector and clustered using Monte Carlo approach. Afterwards, these clustered results are fed to a SOTA Algorithm (combination of self organizing map and hierarchical clustering) for final classification. But in these approaches, combination of self organizing map and hierarchical clustering has the problem in terms of accuracy and computation time of classification, especially when used large dataset for classification. To overcome this problem, we propose a combination of Support Vector Machine (SVM) and Fuzzy C-mean. Our proposed approach provides better result in terms of accuracy, especially when used with large dataset. The proposed method is computationally efficient because fuzzy c-mean clustering is faster and less time consuming as compared to hierarchical clustering.

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