Prediction of Periventricular Leukomalacia Occurrence in Neonates Using a Novel Support Vector Machine Classifier Optimization Method

2015 ◽  
Author(s):  
Dieter Bender ◽  
Ali Jalali ◽  
Daniel J. Licht ◽  
C. Nataraj
Keyword(s):  
Support Vector Machine ◽  
Periventricular Leukomalacia ◽  
Optimization Method ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Stage ◽  
Svm Algorithm ◽  
Initial Idea ◽  
Leave One Out

Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). Our previous study showed that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This study fully develops the initial idea using the reliable Leave-One-Out Cross-validation (LOOCV) technique. The work presented in this paper confirms previous results and improves the performance of the SVM even further. In addition, using the LOOCV technique, the computational time is decreased and the structure of the algorithm simplified, making this framework more feasible. Furthermore, we evaluate the performance of the resulting optimized SVM classifier on an unseen set of data. This demonstrates that the developed SVM algorithm outperforms normal SVM type classifiers without any loss of generalization.

Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine

2019 ◽  
Vol 11 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Ping Zhong ◽  
Mengdi Li ◽  
Kai Mu ◽  
Juan Wen ◽  
Yiming Xue
Keyword(s):  
Support Vector Machine ◽  
Ridge Regression ◽  
Optimization Method ◽  
Least Square ◽  
Computational Time ◽  
Support Vector ◽  
Image Steganalysis ◽  
Linear Discriminant ◽  
Feature Sets ◽  
Proximal Support Vector Machine

This article presents the linear Proximal Support Vector Machine (PSVM) to the image steganalysis, and further generates a very efficient method called PSVM-LSMR through implementing PSVM by the state-of-the-art optimization method Least Square Minimum-Residual (LSMR). Also, motivated by extreme learning machine (ELM), a nonlinear algorithm PSVM-ELM is proposed for the image steganalysis. It is shown by the experiments with the wide stego schemes and rich steganalysis feature sets in both the spatial and JPEG domains that the PSVM can achieve comparable performance with Fisher Linear Discriminant (FLD) and ridge regression, and its computational time is far more less than that of them on large feature sets. The PSVM-LSMR is comparable to Ridge Regression implemented by LSMR (RR-LSMR), and both of them require the least computational time among all the competitions when dealing with medium or large feature sets. The nonlinear PSVM-ELM performs comparably or even better than FLD and ridge regression for the spatial domain steganographic schemes, and its computational time is apparently less than that of them on large feature sets.

scholarly journals Comparison of Random Forest and Support Vector Machine for Indonesian Tweet Complaint Classification

2019 ◽  
pp. 202-207 ◽  
Author(s):  
Desi Ramayanti
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Text Classification ◽  
Research Area ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Documents ◽  
Case Organization

In digital business, the managerial commonly need to process text so that it can be used to support decision-making. The number of text documents contained ideas and opinions is progressing and challenging to understand one by one. Whereas if the data are processed and correctly rendered using machine learning, it can present a general overview of a particular case, organization, or object quickly. Numerous researches have been accomplished in this research area, nevertheless, most of the studies concentrated on English text classification. Every language has various techniques or methods to classify text depending on the characteristics of its grammar. The result of classification among languages may be different even though it used the same algorithm. Given the greatness of text classification, text classification algorithms that can be implemented is the support vector machine (SVM) and Random Forest (RF). Based on the background above, this research is aimed to find out the performance of support vector machine algorithm and random forest in classification of Indonesian text. 1. Result of SVM classifier with cross validation k-10 is derived the best accuracy with value 0.9648, however, it spends computational time as long as 40.118 second. Then, result of RF classifier with values, i.e. 'bootstrap': False, 'min_samples_leaf': 1, 'n_estimators': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None is achieved accuracy is 0.9561 and computational time 109.399 second.

scholarly journals Support Vector Machine optimization with fractional gradient descent for data classification

2021 ◽  
Vol 2 (1) ◽  
pp. 1-6
Author(s):  
Dian Puspita Hapsari ◽  
Imam Utoyo ◽  
Santi Wulan Purnami
Keyword(s):  
Gradient Descent ◽  
Large Scale ◽  
Computing Time ◽  
Data Classification ◽  
Optimization Method ◽  
Descent Method ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Gradient Descent Method

Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.

Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Haitham S. Mohammed ◽  
Hagar M. Hassan ◽  
Michael H. Zakhari ◽  
Hassan Mostafa ◽  
Ebtesam A. Mohamad
Keyword(s):  
Support Vector Machine ◽  
Seizure Detection ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Features ◽  
Linear Feature ◽  
Non Linear ◽  
Before And After ◽  
Sensitivity Specificity

Abstract Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies the disease. The accurate detection of seizures is a crucial step in any procedure of treatment. In the present study, electrocorticogram (ECoG) signals were recorded from awake and freely moving animals implanted with cortical electrodes before and after pentylenetetrazol, the chemo-convulsant injection. ECoG signals were segmented into 4-s epochs and labeled. Twenty-four linear and non-linear features were extracted from the time and frequency domains of the ECoG signals. The extracted features either individually or in combinations were fed to an automatic support vector machine (SVM) classification system. SVM classifier was trained with 5 min of ictal and non-ictal labeled ECoG signals to build the hyperplane that separates two sets of training signals. Sensitivity, specificity, and accuracy were determined for the testing dataset using the different feature combinations. It has been found that some linear features either individually or in combinations outperform non-linear features in terms of the accuracy for seizure detection. The maximum accuracy achieved by the system was 95.3% and has been obtained only after linear and non-linear features were combined. ECoG signals were classified without pre-processing or removal of artifacts to reduce the required computational time to be suitable for online implementation purposes. This may prove the detection system’s robustness and supports its use in online seizure detection protocols.

Genetic Optimization of Energy Consumption of Pellet Shaft Furnace Combustor Based on Support Vector Machine (SVM)

2014 ◽  
Vol 12 (1) ◽  
pp. 205-214 ◽  
Author(s):  
Xi Chen ◽  
Wenqi Zhong ◽  
Tiancai Wang ◽  
Fei Liu ◽  
Zhi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Energy Consumption ◽  
Shaft Furnace ◽  
Fitness Function ◽  
Optimization Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Genetic Optimization ◽  
Gas Consumption

Abstract Investigation on optimization of pellet shaft furnace based on the combination of genetic algorithm and support vector machine (SVM) is carried out. A SVM classifier model is developed to map the complex nonlinear relationship between operating parameters and the quality indexes of fired pellet, and a genetic algorithm is adapted in the energy optimization with the fitness function based on the SVM classifier model. This method can reduce the energy consumption while maintaining the fired pellet quality stable. The results show that the accuracy of the SVM classifier model is satisfied and the gas consumption can be reduced by 4% per ton of green pellets with this optimization method.

Prediction of Periventricular Leukomalacia Occurrence in Neonates Using a Novel Unsupervised Learning Method

2014 ◽  
Author(s):  
Dieter Bender ◽  
Ali Jalali ◽  
C. Nataraj
Keyword(s):  
Active Learning ◽  
Learning Algorithm ◽  
Periventricular Leukomalacia ◽  
Support Vector ◽  
Complex Nature ◽  
Training Set ◽  
Learning Stage ◽  
Supervised Training ◽  
Svm Algorithm ◽  
Initial Hypothesis

Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). A preceding study indicated that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This preliminary work, as well as the complex nature of the PVL data suggested integration of the active learning algorithm into the overall SVM framework. The present study supports this initial hypothesis and shows that active learning SVM type classifier performs considerably well and outperforms normal SVM type classifiers when dealing with clinical data of high dimensionality.

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%.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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