Individual Support Vector Machine Research Articles

Hard Voting Approach using SVM, Naïve Bays and Decision Tree for Kurdish Fake News Detection

In today's age of excessive information, the proliferation of fake news has emerged as a significant challenge, eroding the trustworthiness of news sources and posing serious threats to society. With the advent of social media and the internet, false information spreads more easily, necessitating the development of effective methods to identify and prevent its dissemination.While machine learning models have been widely employed to classify text data as authentic or fake, the majority of existing research has primarily focused on well-resourced languages such as English, leaving low-resourced languages like Kurdish largely overlooked. To address this gap, our proposed work introduces a novel approach: a hard voting ensemble method that combines the insights of four weak learners. By fine-tuning these weak learners for optimal performance, our approach achieves enhanced accuracy compared to the current state-of-the-art methods. Specifically, our findings demonstrate the effectiveness of the hard voting approach using a combination of Support Vector Machines (SVM), Decision Trees (DT), and Naive Bayes (NB) classifiers, resulting in an impressive accuracy rate of 89.73%. This outperforms the individual SVM classifier and underscores the potential of ensemble techniques in fake news detection.

0537 Incident Hypertension Prediction in Obstructive Sleep Apnea using Machine Learning

Abstract Introduction Obstructive sleep apnea (OSA) is associated with hypertension due to intermittent hypoxia and sleep fragmentation. Due to the complex pathogenesis of hypertension, it is difficult to predict incident hypertension associated with OSA. A Machine Learning (ML) model to predict incident hypertension identified up to five years after the diagnosis of OSA by polysomnography developed. Methods Polysomnography provides time-series data on multiple physiological signals. We used the sleep heart health study (SHHS) cohort, where 4,797 participants had OSA. After excluding participants with pre-existing hypertension at baseline, the sample size was 2,652. 1,814 participants with follow-up data at 5 years were included (911/1,814, 50% with incident hypertension). In addition to clinical data (i.e. age and race), features extracted from polysomnography (heart rate variability, HRV calculated based on the electrocardiography R-R interval), electroencephalography delta power, statistical information (i.e., mean and standard deviation of signals), and heart rate periodicity functions fed to support vector machine (SVM) ML model to train and validate. The polysomnography features were calculated over the 30-second epochs identified based on respiratory events and EEG arousal and respiratory events annotation, and their corresponding parts in other signals based on sampling frequency. Technical artifacts in oxygen saturation and ECG were reconstructed with the interpolation method and removed from the signal respectively. The SVM is a robust ML method trained in an iterative fashion to find the global optimum. In comparison to the Deep Neural Network (DNN) approaches, SVMs results are interpretable. Each polysomnography signal and its corresponding features were trained on a separate SVM, followed by a fusion of the SVM results. The final results were fused by voting of individual SVM results. Results The SVM ML model thus far has achieved a test accuracy (area under the curve, AUC) of 66.06%, sensitivity 63.21%, and specificity 68.9%. Conclusion This proof-of-concept study suggests that supervised ML models, such as the SVM, may be useful in predicting incident hypertension associated with OSA. Further research is required regarding optimal input features to boost the accuracy, followed by external validation of the model in additional OSA cohorts. Support (if any) Research support 1R56HL157182, NIH/NHLBI

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

The effect of various parameters on the flexural strength (FS) of ultra-high-performance concrete (UHPC) is an intricate mechanism due to the involvement of several inter-dependent raw ingredients. In this digital era, novel artificial intelligence (AI) approaches, especially machine learning (ML) techniques, are gaining popularity for predicting the properties of concrete composites due to their better precision than typical regression models. In addition, the developed ML models in the literature for FS of UHPC are minimal, with limited input parameters. Hence, this research aims to predict the FS of UHPC considering extensive input parameters (21) and evaluate each their effect on its strength by applying advanced ML approaches. Consequently, this paper involves the application of ML approaches, i.e., Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Gradient Boosting (GB), to predict the FS of UHPC. The GB approach is more effective in predicting the FS of UHPC precisely than the SVM and MLP algorithms, as evident from the outcomes of the current study. The ensembled GB model determination coefficient (R2) is 0.91, higher than individual SVM with 0.75 and individual MLP with 0.71. Moreover, the precision of applied models is validated by employing the k-fold cross-validation technique. The validity of algorithms is ensured by statistical means, i.e., mean absolute error and root mean square errors. The exploration of input parameters (raw materials) impact on FS of UHPC is also made with the help of SHAP analysis. It is revealed from the SHAP analysis that the steel fiber content feature has the highest influence on the FS of UHPC.

Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China

Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

Semi-supervised style transfer mapping-based framework for sEMG-based pattern recognition with 1- or 2-DoF forearm motions

BackgroundThe development of body-worn sensors has made it easier to measure surface electromyograms (sEMGs) from individuals and to employ sEMG-based interfaces for user-centric health monitoring, rehabilitation, human augmentation, and amusement. However, it remains difficult to measure large amounts of sEMG data from a user (target). Thus, the development of a subject-to-subject transfer framework that uses the information that is available from other people (source) is challenging work. ObjectivesIn this study, we propose a subject-to-subject transfer framework, which includes the following four steps: (i) the construction of individual support vector machines (SVMs) from the source subjects; (ii) the selection of effective classifiers for the target based on the individual SVM classification results; (iii) the linear projection of the target data into the source subject data using the semi-supervised style transfer mapping algorithm; and (iv) an ensemble strategy of class probabilities for the selected classifiers. MethodsTo evaluate the performance of proposed framework, we collected 8-class, 1-DoF and 14-class, 2-DoF sEMG datasets that were acquired from the same 25 subjects using an eight-channel wearable device. The classifiers were trained with time-domain and autoregressive features. ResultsOur proposed method exhibited superior performance on both datasets compared to a conventional transfer framework using covariate shift adaptation. The sEMG datasets and sample codes used in this study are publicly available at https://github.com/Suguru55/SS-STM_for_MyoDatasets.

Noise robust automatic heartbeat classification system using support vector machine and conditional spectral moment.

Heartbeat classification is central to the detection of the arrhythmia. For the effective heartbeat classification, the noise-robust features are very significant. In this work, we have proposed a noise-robust support vector machine (SVM) based heartbeat classifier. The proposed classifier utilizes a novel noise-robust morphological feature which is based on the conditional spectral moment (CSM) of the heartbeat. In addition to the proposed CSM feature, we have also employed the existing RR interval, the wavelets, and the higher-order statistics (HOS) based temporal and morphological feature sets. The noise-robustness test of the proposed CSM and all the studied feature sets is performed for the SVM based heartbeat classifier. Further, we have studied the significance of combining these temporal and morphological features on the final classification performance. For this purpose, the individual SVMs were trained for each of the feature set. The final classification is based on the ensemble of these individual SVMs. Various combining scheme such as sum, majority, and product rules are employed to ensemble the result of the individually trained SVMs. The experimental results show the noise-robustness of the proposed CSM feature. The proposed classifier gives improved overall performance compared to the existing heartbeat classification systems.

A boosted SVM classifier trained by incremental learning and decremental unlearning approach

The Support Vector Machines (SVM) classifier is a margin-based supervised machine learning method used for categorization and classification tasks. A Linear SVM classifier uses linear Kernels while a non-linear SVM classifier adopts non-linear Kernels. The Linear SVM classifier is considered an efficient technique, especially for big datasets of high dimensionality in various applications, such as document categorization, time-series classification, outliers’ detection, to name a few. It is much faster to train a linear SVM classifier than a non-linear SVM classifier. For large-scale datasets with various shapes, configurations, and distributions, the computational complexity of training a non-linear SVM classifier is continuously evolving. Current research methods have introduced various problem formulations, solvers, and strategies for speeding up the training process of a non-linear SVM classifier. However, solving a quadratic programming (QP) problem is still challenging, especially for “Big Data”, which poses a great challenge for traditional methods to train a non-linear classifier efficiently. In this paper, we are proposing a novel boosting algorithm to enhance the performance of weak non-linear SVM classifiers using the notion of incremental learning and decremental unlearning. Experimental results over artificial and real datasets with different sizes, shapes, and distributions show that the proposed ensemble boosting algorithm outperforms the individual SVM classifiers measured by the classification accuracy and the Speedup.

Formulating Ensemble Learning of SVMs Into a Single SVM Formulation by Negative Agreement Learning

When a fixed number of support vector machines (SVMs) are taken as the base learners, an attempt to diversify them should be encouraged to achieve a satisfactory ensemble. In this article, by means of a negative agreement learning (NAL) strategy, a new SVM-based ensemble framework is proposed to simultaneously enhance the diversity of SVMs in the ensemble and suppress the training error of the ensemble. The proposed ensemble framework is theoretically derived to have distinctive merits: 1) the ensemble and each of its individual SVM base learner are trained in a joint manner rather than in an independent manner and 2) the NAL strategy facilitates the formulation of the ensemble of SVMs as one single SVM; thus, abundant advances in the training of SVM can be conveniently applied to the proposed ensemble learning of SVMs and there is no need to design special optimization techniques for the involved ensemble learning. Extensive experimental studies demonstrate the effectiveness of the proposed ensemble framework of SVMs.

Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a -mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level -mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.

A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia.

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM—radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map.

Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques

Background and objectiveGlaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinical methods and imaging techniques are manual and require skilled supervision. For the purpose of mass screening, an automated system is needed for glaucoma diagnosis which is fast, accurate, and helps in reducing the burden on experts. MethodsIn this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approach for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from the input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistical features from each of the bit planes of the individual channels. Thirdly, these features from the individual channels are fed separately to three different support vector machines (SVMs) for classification. Finally, the decisions from the individual SVMs are fused at the decision level to classify the input fundus image into normal or glaucoma class. ResultsOur experimental results suggest that the proposed approach is effective in discriminating normal and glaucoma cases with an accuracy of 99.30% using 10-fold cross validation. ConclusionsThe developed system is ready to be tested on large and diverse databases and can assist the ophthalmologists in their daily screening to confirm their diagnosis, thereby increasing accuracy of diagnosis.

GA-AdaBoostSVM classifier empowered wireless network diagnosis

Self-healing is one of the most important parts in self-organizing mobile communication network. It focuses on detecting the decline of service quality and finding out the cause of network anomalies and repairing it with high automation. Diagnosis is a particularly important task which identifies the fault cause of problematic cells or regions. To perform the diagnosis, this paper presents two modified ensemble classifiers by using Support Vector Machine (SVM) with different kernels, i.e., SVM with the radial basis function (RBF) kernel (RBFSVM in short) and SVM with the linear kernel (LSVM in short), as component classifier in Adaptive Boosting (AdaBoost), and we call the two ensemble classifiers as Adaptive Boosting based on RBFSVM (AdaBoostRBFSVM in short) and Adaptive Boosting based on linear kernel (AdaBoostLSVM in short). Different with previous AdaBoostSVM classifiers using weak component classifiers, in this paper, the performance of the classifiers is adaptively improved by using moderately accurate SVM classifiers (the training error is less than 50%). To solve the accuracy/diversity dilemma in AdaBoost and get good classification performance, the training error threshold is regulated to adjust the diversity of classifier, and the parameters of SVM (regularization parameter C and Gaussian width σ) are changed to control the accuracy of classifier. The accuracy and diversity will be well balanced through reasonable parameter adjustment strategy. Results show that the proposed approaches outperform individual SVM approaches and show good generalization performance. The AdaBoostLSVM classifier has higher accuracy and stability than LSVM classifier. Compared with RBFSVM, the undetected rate and diagnosis error rate of AdaBoostRBFSVM decrease slightly, but the false positive rate does reduce a lot. It means that the AdaBoostRBFSVM classifier is indeed available and can greatly reduce the number of normal class samples that have been wrongly classified. Therefore, the two ensemble classifiers based on the SVM component classifier can improve the generalization performance by reasonably adjusting the parameters. To set the parameter values of component classifiers in a more reasonable and effective way, genetic algorithm is introduced to find the set of parameter values for the best classification accuracy of AdaBoostSVM, and the new ensemble classifier is called AdaboostSVM based on genetic algorithm (GA-AdaboostSVM in short) (including AdaboostLSVM based on genetic algorithm and AdaboostRBFSVM based on genetic algorithm). Results show that GA-AdaboostSVM classifiers have a lower overall error than AdaboostSVM classifiers. Genetic algorithm could help to achieve a more optimal performance of the ensemble classifiers.

Process service quality evaluation based on Dempster-Shafer theory and support vector machine

Human involvement influences traditional service quality evaluations, which triggers an evaluation’s low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors. Preprocessing steps such as feature simplification and normalization are reduced. Based on three individual SVM models, the basic probability assignments (BPAs) are constructed, which can help the evaluation in a qualitative and quantitative way. The process service quality evaluation results are validated by the Dempster rules; the decision threshold to resolve conflicting results is generated from three SVM models. A case study is presented to demonstrate the effectiveness of the SVMs-DS method.

Wavelet Transform Features to Hybrid Classifier for Detection of Neurological-Disordered Voices

Neurological problems may lead to speech-related disorders because of improper movements of vocal fold or incomplete closure of glottis. This may alter the acoustic characteristics of voice signal, which may provide valuable clues for diagnosing certain neurological diseases. In this work, the wavelet transform and Mel-frequency cepstral coefficients, which are features of short-time analysis techniques, fused with the time-domain features, and given to a hybrid model designed using Gaussian mixture model (GMM) and support vector machines (SVM). The fusion of features and fusion of different classifiers are carried out to avoid the generation of large feature space, complexity, and delayed results. Linear predictive coded–Mel-frequency cepstral coefficients computed for selected 6-level discrete wavelet transform are given to GMM. The output of which is combined with SVM scores obtained with time-domain features as input and given to another SVM, which makes the decision to classify the data as normal or neurological-disordered voice. It is observed that this hybrid classifier model has shown an improvement with a classification accuracy of 94.3% compared with individual SVM classifier with time-domain features with 81.43% and the GMM-SVM classifier with 85.71%.

Automatic Human Facial Expression Recognition Based on Integrated Classifier From Monocular Video with Uncalibrated Camera

An automatic recognition framework for human facial expressions from a monocular video with an uncalibrated camera is proposed. The expression characteristics are first acquired from a kind of deformable template, similar to a facial muscle distribution. After associated regularization, the time sequences from the trait changes in space-time under complete expressional production are then arranged line by line in a matrix. Next, the matrix dimensionality is reduced by a method of manifold learning of neighborhood-preserving embedding. Finally, the refined matrix containing the expression trait information is recognized by a classifier that integrates the hidden conditional random field (HCRF) and support vector machine (SVM). In an experiment using the Cohn–Kanade database, the proposed method showed a comparatively higher recognition rate than the individual HCRF or SVM methods in direct recognition from two-dimensional human face traits. Moreover, the proposed method was shown to be more robust than the typical Kotsia method because the former contains more structural characteristics of the data to be classified in space-time

Dempster-Shafer evidence theory for multi-bearing faults diagnosis

Support vector machines (SVMs) are frequently used in automated machinery faults diagnosis to classify multiple machinery faults by handling a high number of input features with low sampling data sets. SVMs are well known for fault detection that involves binary fault classifications only (i.e., healthy vs. faulty). However, when SVMs are used for multi-faults diagnostics and classification, they result in a drop in classification accuracy; this is because the adaptation of SVMs for multi-faults classifications requires the reduction of the multiple classification problem into multiple subsets of binary classification problems that result in many contradictory results from each individual SVM model. To overcome this problem, a novel SVM-DS (Dempster-Shafer evidence theory) model is proposed to resolve conflicting results generated from each SVM model and thus increase the classification accuracy. The analysis of results shows that the proposed SVM-DS model increased the accuracy of the fault diagnosis model from 76% to 94%, as SVM-DS continuously refines and eliminates all conflicting results from the original SVM model. The proposed SVM-DS model is found to be more accurate and effective in handling multi-faults diagnostic and classification problems commonly faced in the industries, as compared to the original SVM method.

Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features

We present a novel image-analysis based method for automatically distinguishing low, intermediate, and high grades of breast cancer in digitized histopathology. A multiple level feature set, including pixel-, object-, and semantic-level features derived from convolutional neural networks (CNN), is extracted from 106 hematoxylin and eosin stained breast biopsy tissue studies from 106 women patients. These multi-level features allow not only characterization of cancer morphology, but also extraction of structural and interpretable information within the histopathological images. In this study, an improved hybrid active contour model based segmentation method was used to segment nuclei from the images. The semantic-level features were extracted by a CNN approach, which described the proportions of nuclei belonging to the different grades, in conjunction with pixel-level (texture) and object-level (architecture) features, to create an integrated set of image attributes that can potentially outperform either subtype of features individually. We utilized a cascaded approach to train multiple support vector machine (SVM) classifiers using combinations of feature subtypes to enable the possibility of maximizing the performance by leveraging different feature sets extracted from multiple levels. Final class (cancer grade) was determined by combining the scores produced by the individual SVM classifiers. By employing a light (three-layer) CNN model and parallel computing, the presented approach is computationally efficient and applicable to large-scale datasets. The method achieved an accuracy of 0.92 for low versus high, 0.77 for low versus intermediate, and 0.76 for intermediate versus high, and an overall accuracy of 0.69 when discriminating low, intermediate, and high grades of histopathological breast cancer images. This suggested that our grading method could be useful in developing a computational diagnostic tool for differentiating breast cancer grades, which might enable objective and reproducible alternative for diagnosis.

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control (NMPC) approach based on support vector machine (SVM) and genetic algorithm (GA) for multiple-input multiple-output (MIMO) nonlinear systems. Individual SVM is used to approximate each output of the controlled plant. Then the model is used in MPC control scheme to predict the outputs of the controlled plant. The optimal control sequence is calculated using GA with elite preserve strategy. Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

Gaussian image pyramid based texture features for classification of microscopic images of hardwood species

This paper presents a texture feature based approach for hardwood species classification. The three existing feature extraction techniques such as local binary pattern (LBP), local configuration pattern (LCP) and local phase quantization (LPQ) are integrated here with Gaussian image pyramid (GIP) which results in improvement of classification accuracy. The texture features are extracted at seven different decomposition levels generated by the GIP. These texture features are fed as input to linear support vector machine (SVM) classifier that uses 10-fold cross validation approach of classification. The results of combination of GIP decomposition with individual texture feature extraction techniques and linear SVM classifier have been compared. The comparison yields that Gaussian image pyramid based local phase quantization (GPLPQ) texture feature extraction technique using third (3rd) level of image decomposition results in the best classification accuracy of 98.60% for hardwood species. The proposed integration of GIP and texture feature extraction techniques also proves to be an effective tool of classification for texture surface database. For texture surface database, Gaussian image pyramid based rotation invariant uniform local configuration pattern (GPLCPriu2) has achieved 98.00% classification accuracy.

Manifestation of LiDAR-Derived Parameters in the Spatial Prediction of Landslides Using Novel Ensemble Evidential Belief Functions and Support Vector Machine Models in GIS

Landslide susceptibility mapping is indispensable for disaster management and planning development operations in mountainous regions. The potential use of light detection and ranging (LiDAR) data was explored in this study for deriving landslide-conditioning factors for the spatial prediction of landslide-susceptible areas in a landslide-prone area in Ulu Klang, Malaysia. Nine landslide-conditioning factors, such as altitude, slope, aspect, curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), sediment transport index (STI), and slope length (SL), were directly derived from LiDAR for landslide-susceptibility mapping. The main objective of this research was to propose a novel ensemble landslide susceptibility mapping method to enhance the performance of individual methods of support vector machine (SVM) and evidential belief function (EBF). SVM is time-consuming when various data types, such as nominal, scale, and ordinal, are used. This characteristic of the individual SVM method is not optimal for hazard modeling. This drawback can be resolved by assessing the effects of the classes of each conditioning factor on landslide occurrence through a data-driven EBF method. Hence, EBF was applied in this study, and weights were acquired for the classes of each conditioning factor. The conditioning factors were reclassified based on the attained weights and entered into SVM as a scale to evaluate the correlation between landslide occurrence and each conditioning factor. Four SVM kernel types [radial basis function kernel (RBF), sigmoid kernel (SIG), linear kernel (LN), and polynomial kernel (PL)] were tested to explore the efficiency of each kernel in SVM modeling. The efficiencies of the ensemble EBF and SVM methods were examined through area under curve (AUC). The RBF kernel obtained better results than the other kernel types. The success and prediction rates obtained from the validation results of ensemble EBF and RBF-SVM method were 83.04% and 80.04%, respectively. The proposed novel ensemble method reasonably accelerated the processing and enhanced the results by combining the advantages of both methods.