Variant Of Support Vector Machine Research Articles

Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.

Performance Comparison between SVM and LS-SVM for Rice Leaf Disease detection

INTRODUCTION: Automatic detection of rice plant diseases at early stage from its images is quite beneficial over traditional verification methods.
 OBJECTIVES: Recent years machine learning (ML) approaches are more efficient in disease classification task. In current generation the statistical machine learning algorithm which shows state-of-arts performance is Support Vector Machine (SVM) and variants of SVM.
 METHODS: SVM has an excellent learning performance for linear and non-linear data samples. It works for Quadratic Programming Problems (QPP) due to which it has the drawback of computational complexity. However QPP can be solved linearly with the help of Least Square SVM(LS-SVM) approach. In LS-SVM the epsilon tube and slack variables of SVM are replaced with error variables. The distance is calculated by error square value.
 RESULTS: In this research performance comparison is made between SVM and LS-SVM for rice leaf diseases such as Bacterial Leaf Blight (BLB), Brown spot(BS), Leaf smut(LS) and Leaf Blast using two datasets (DS1 and DS2).Accuracy of LS-SVM is found to be 91.3% and 98.87% for DS1 and DS2 respectively whereas accuracy of SVM is 83.3% and 98.75% for DS1 and DS2 respectively.
 CONCLUSION: Performance of LS-SVM outperformed than SVM in terms of accuracy.

SUPPORT VECTOR MACHINE MODELS FOR DIABETIC RETINOPATHY DIAGNOSIS AND GRADING

This work applies five variants of the support vector machine to the classification of the various stages of nonproliferative diabetic retinopathy. Four hundred eye fundus images from the Messidor repository are preprocessed and thirteen features extracted. The features best suited as inputs for support vector machine classification are identified. Initially, binary classification of severe nonproliferative diabetic retinopathy alone versus a normal eye is performed, achieving an accuracy of 97.44% using the standard support vector machine with Gaussian kernel and when optimized for accuracy. When optimized for sensitivity, the twin bounded support vector machine achieves the highest sensitivity of 99.06%. Then multiclass grading into all four stages of nonproliferative diabetic retinopathy is performed. Best performance with regards to four performance metrics, namely accuracy, sensitivity, specificity, and precision is achieved with the twin bounded support vector machine variant, when one-versus-one decision configuration is used in combination with a novel decision strategy that includes accumulated distances from the decision hyperplanes in the decision algorithm. The results compare favorably with data published in the literature.

A stable variant of linex loss SVM for handling noise with reduced hyperparameters

Support Vector Machine (SVM) primarily uses the hinge loss function with maximum margin. The boundary instances determine the separating hyperplanes. However, in real-world scenarios, noisy instances around the decision boundary can degrade the performance of hinge SVM. To address this issue, researchers introduced the pinball SVM, which uses all the instances to determine the separating hyperplane and has a discontinuous piecewise linear loss function. A more recent variant, the linex loss SVM with NAG, employs a combination of linear and exponential loss functions, resulting in an asymmetric and continuous loss function. Due to the employment of Nesterov accelerated gradient (NAG), the older linex SVM formulation was unstable. This study presents a novel noise-robust variant of linex SVM, termed linexSVM, which is stable in performance compared to the linex SVM with NAG. Additionally, the proposed linexSVM has fewer hyperparameters in the dual formulation than other noise-robust SVM variants, such as pinball SVM, ϵ-insensitive pinball SVM, and ϵ1,ϵ2 pinball SVM. We evaluated the performance of the proposed linexSVM, hinge SVM, and pinball SVM using the UCI dataset repository. The experimental results indicate that the proposed linexSVM outperforms hinge and pinball loss SVMs on datasets with and without noise.

Support vector machine in structural reliability analysis: A review

Support vector machine (SVM) is a powerful machine learning technique relying on the structural risk minimization principle. The applications of SVM in structural reliability analysis (SRA) are enormous in the recent past. There are review articles on machine learning-based methods that partly discussed the development of SVM for SRA applications along with other machine learning methods. However, there is no dedicated review on SVM for SRA applications. Thus, a review article on the implementation of various SVM approaches for SRA applications will be useful. The present article provides a synthesis and roadmap to the growing and diverse literature, specifically the classification and regression-based support vector algorithms in SRA applications. In doing so, different advanced variants of SVM in SRA applications and hyperparameter tuning algorithms are also briefly discussed. Following the detailed review studies, future opportunities and challenges in the area of applications are summarized. The review in general reveals that the SVM in SRA applications is getting thrust as it has an excellent capability of handling high-dimensional problems utilizing relatively lesser training data. The review article is expected to enhance the state-of-the-art developments of support vector algorithms for SRA applications.

Multicycle disassembly-based decomposition algorithm to train multiclass support vector machines

Employing the classic optimization solver to train a multiclass support vector machine (SVM) requires prohibitive training time as the sample size and number of categories increase. It has been proposed to develop the corresponding decomposition algorithm (DA) as it is efficient for training SVMs. However, the dual problem of multiclass SVM comprises complex constraints that complicate DA design, so no corresponding DA has yet been developed. We propose a multicycle disassembly-based DA (MCD-DA) to efficiently solve the training problem of multiclass SVM. First, a graph model is constructed to re-express the constraints in multiclass SVM. Then, the original complex feasible region is partitioned into several simple sub-feasible regions, and multiple cycle-based disassembly strategies are designed to update the working variables analytically within each specific sub-feasible region. We mathematically verify that MCD-DA can stop within a finite number of cycle disassemblies and reach the τ-optimal solution satisfying relaxed Karush–Kuhn–Tucker conditions. Remarkably, MCD-DA as a universal decomposition algorithm can be used to solve many other SVM variants, including C-SVM, v-SVM, and one-class SVM. Experimental results using six UCI datasets demonstrate that MCD-DA outperforms typical optimization algorithms for more sample cases.

Over-relaxed multi-block ADMM algorithms for doubly regularized support vector machines

As a classical machine learning model, support vector machine (SVM) has attracted much attention due to its rigorous theoretical foundation and powerful discriminative performance. The doubly regularized SVM (DRSVM) is an important variant of SVM based on elastic-net regularization, which considers both the sparsity and stability of the model. To tackle the problems of explosive increases in data dimensions and data volume, the alternating direction method of multipliers (ADMM) algorithm can be used to train the DRSVM model. ADMM is an effective iterative algorithm for solving convex optimization problems by decomposing a large issue into a series of solvable subproblems, which is also well suited for distributed computing. However, lack of guaranteed convergence and slow convergence rate are two critical limitations of ADMM. In this paper, a 3-block ADMM algorithm based on the over-relaxation technique is proposed to accelerate DRSVM training, namely, the over-relaxed DRSVM (O-RDRSVM). The main strategy of the over-relaxation technique is to further append the information from the previous iteration to the next iteration to improve the convergence of ADMM. We also propose a distributed version of O-RDRSVM to handle parallel and distributed computing faster, termed DO-RDRSVM. Moreover, we develop a fast O-RDRSVM algorithm (FO-RDRSVM) and a fast DO-RDRSVM algorithm (FDO-RDRSVM), which further reduce the computational cost of O-RDRSVM and DO-RDRSVM by employing the matrix inversion lemma. The convergence analyses ensure the effectiveness of our algorithms for DRSVM training. Finally, extensive experiments on public datasets demonstrate the advantages of our algorithms in terms of convergence rate and training time while maintaining accuracy and sparsity comparable to those of previous works.

Slack-Factor-Based Fuzzy Support Vector Machine for Class Imbalance Problems

Class imbalance and noisy data widely exist in real-world problems, and the support vector machine (SVM) is hard to construct good classifiers on these data. Fuzzy SVMs (FSVMs), as variants of SVM, use a fuzzy membership function both to reflect the samples’ importance and to remove the impact of noises, and employ cost-sensitive technology to address the class imbalance. They can handle the noise and class imbalance problems in many cases, however, the fuzzy membership functions are often affected by the class imbalance data, leading to inaccurate measures for samples’ performance and affecting the performance of FSVMs. To solve this problem, we design a new fuzzy membership function and combine it with cost-sensitive learning to deal with the class imbalance problem with noisy data, named Slack-Factor-based FSVM (SFFSVM). In SFFSVM, the relative distances between samples and an estimated hyperplane, called slack factors, are used to define the fuzzy membership function. To eliminate the impact of class imbalance on the function and gain more accurate samples’ importance, we rectify the importance according to the positional relationship between the estimated hyperplane and the optimal hyperplane of the problem, and the slack factors of samples. Comprehensive experiments on artificial and real-world datasets demonstrate that SFFSVM outperforms other comparative methods on F1, MCC, and AUC-PR metrics.

Classification of Thoracic Diseases Based on Chest X-ray Images Using Kernel Support Vector Machine

Machine learning is the leading field of artificial intelligence that has achieved expert-level performance. Diagnosis and treatment of various medical diseases have led to advancements in medical imaging. Chest X-ray-based thoracic disease classification or identification is one of the potential applications in medical imaging based on machine learning. The study consists of 112,120 images of 30,804 individual patients with fourteen thoracic disease labels, which encapsulated the support vector machine (SVM). We have considered 04 kernels in SVM, namely, linear (L-SVM), polynomial (P-SVM), radial basis (R-SVM), and hyperbolic tangent (H-SVM) for classification of thoracic diseases based on X-ray images. To reduce the dimensionality and outliers from the SVM, variants are coupled with novel fast principal component analysis (FPCA). It appears that there is a significant p ≤ 0.05 difference between SVM variants where P-SVM and R-SVM next in order outperforms on most of the disease identification models with average validated classification accuracy ranging from 92% to 98%. The average calibrated accuracy ranges from 99.5% and reaches to 100% in most of the cases. The study is worth investigating as it is good for radiologists as they will be able to classify the diseases and it will help in improving and enhancing different medical techniques.

Analysis of recent advancements in support vector machine

SummaryThe current researches are primarily focused on optimizing the available classification methods in support vector machines (SVMs). The basic idea of this article to highlight the fundamentals of the proposed SVM variants and their optimization techniques. In this article, we have collectively identified the major issues in different variations of SVMs. This article has discussed a detailed explanation of the optimization and advancement of SVM and its kernel variants. The study elaborates on certain SVM optimization issues like model selection, novelty detection and so forth. In contrast, it discussed the identification of problems raises after every optimization and advancement of SVM. This is the first novel attempt to explain the optimization raises for SVMs. This article is about the majority of the necessary advancements in the optimization and cost‐effectiveness of computational algorithms of SVM with their detailed computational analysis. The approaches of such study helps to understand the optimization challenges in SVM.

Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE).

Healthcare systems have been under immense pressure since the beginning of the COVID-19 pandemic; hence, studies on using machine learning (ML) methods for classifying ICU admissions and resource allocation are urgently needed. We investigated whether ML can propose a useful classification model for predicting the ICU admissions of COVID-19 patients. In this retrospective study, the clinical characteristics and laboratory findings of 100 patients with laboratory-confirmed COVID-19 tests were retrieved between May 2020 and January 2021. Based on patients’ demographic and clinical data, we analyzed the capability of the proposed weighted radial kernel support vector machine (SVM), coupled with (RFE). The proposed method is compared with other reference methods such as linear discriminant analysis (LDA) and kernel-based SVM variants including the linear, polynomial, and radial kernels coupled with REF for predicting ICU admissions of COVID-19 patients. An initial performance assessment indicated that the SVM with weighted radial kernels coupled with REF outperformed the other classification methods in discriminating between ICU and non-ICU admissions in COVID-19 patients. Furthermore, applying the Recursive Feature Elimination (RFE) with weighted radial kernel SVM identified a significant set of variables that can predict and statistically distinguish ICU from non-ICU COVID-19 patients. The patients’ weight, PCR Ct Value, CCL19, INF-β, BLC, INR, PT, PTT, CKMB, HB, platelets, RBC, urea, creatinine and albumin results were found to be the significant predicting features. We believe that weighted radial kernel SVM can be used as an assisting ML approach to guide hospital decision makers in resource allocation and mobilization between intensive care and isolation units. We model the data retrospectively on a selected subset of patient-derived variables based on previous knowledge of ICU admission and this needs to be trained in order to forecast prospectively.

A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns

ABSTRACT Background: Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically actionable interventions in SUD research. Objectives: Inspired by a study of heavy drinkers that collected daily drinking and substance use (ABQ DrinQ), we develop tools to estimate subject-specific risk trajectories of heavy drinking; estimate and perform inference on patient characteristics and time-varying covariates; and present results in easy-to-use Jupyter notebooks. Methods:We recast support vector machines (SVMs) into a Bayesian model extended to handle mixed effects. We then apply these methods to ABQ DrinQ to model alcohol use patterns. ABQ DrinQ consists of 190 heavy drinkers (44% female) with 109,580 daily observations. Results:We identified male gender (point estimate; 95% credible interval: −0.25;-0.29,-0.21), older age (−0.03;-0.03,-0.03), and time varying usage of nicotine (1.68;1.62,1.73), cannabis (0.05;0.03,0.07), and other drugs (1.16;1.01,1.35) as statistically significant factors of heavy drinking behavior. By adopting random effects to capture the subject-specific longitudinal trajectories, the algorithm outperforms traditional SVM (classifies 84% of heavy drinking days correctly versus 73%). Conclusions: We developed a mixed effects variant of SVM and compare it to the traditional formulation, with an eye toward elucidating the importance of incorporating random effects to account for underlying heterogeneity in SUD data. These tools and examples are packaged into a repository for researchers to explore. Understanding patterns and risk of substance use could be used for developing individualized interventions.

EEG‐based automatic multi‐class classification of epileptic seizure types using recurrence plots

AbstractThere is an urgent need to develop an efficient system for accurate recognition of epileptic seizure type that could play a significant role in reducing the adversial effects of the disease. A lot of work is available for EEG based automatic seizure detection but very less attempts have been made towards the classification of variants of seizures. Moreover, none of the authors have included the EEG signals for myoclonic seizure type in their classification studies. Our study aims to propose the automatic machine learning based multi‐class classification system for classifying six types of epileptic seizures (such as absence, focal non‐specific, complex‐partial, generalized, tonic–clonic and including myoclonic too) through the use of recurrence plots (RPs). In our study, we have collected 19‐channel EEG data from a huge database of Temple University Hospital. Two classification modalities have been proposed depending upon the feature extraction approach followed for RPs‐ (a) Traditional approach using the recurrence quantification analysis (RQA) method; (b) Texture based approach using the hybrid method named as Unthresholded Recurrence Plot with Fractal Weighted Local Binary Pattern (URP‐FWLBP), that has been proposed in our work using a combination of Unthresholded RPs (URPs) and Fractal Weighted Local Binary Pattern (FWLBP) method. Thereby, an indirect variant of support vector machine (one‐vs‐rest approach) has been used as the multi‐class classifier in both the approaches. The performance of the proposed modalities has been validated using five‐fold cross‐validation method in the light of seven metrics‐ accuracy, sensitivity, specificity, Matthews correlation coefficient, geometric‐mean, precision and f1‐score. The experimental results show the effectiveness of the proposed system following hybrid URP‐FWLBP method in performing multi‐class epileptic seizure type classification with 100% efficiency, thereby outperforming the traditional RQA method based system as well the existing state‐of‐the‐art systems.

A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services

A cancer pathology report is a valuable medical document that provides information for clinical management of the patient and evaluation of health care. However, there are variations in the quality of reporting in free-text style formats, ranging from comprehensive to incomplete reporting. Moreover, the increasing incidence of cancer has generated a high throughput of pathology reports. Hence, manual extraction and classification of information from these reports can be intrinsically complex and resource-intensive. This study aimed to (i) evaluate the quality of over 80,000 breast, colorectal, and prostate cancer free-text pathology reports and (ii) assess the effectiveness of random forest (RF) and variants of support vector machine (SVM) in the classification of reports into benign and malignant classes. The study approach comprises data preprocessing, visualisation, feature selections, text classification, and evaluation of performance metrics. The performance of the classifiers was evaluated across various feature sizes, which were jointly selected by four filter feature selection methods. The feature selection methods identified established clinical terms, which are synonymous with each of the three cancers. Uni-gram tokenisation using the classifiers showed that the predictive power of RF model was consistent across various feature sizes, with overall F-scores of 95.2%, 94.0%, and 95.3% for breast, colorectal, and prostate cancer classification, respectively. The radial SVM achieved better classification performance compared with its linear variant for most of the feature sizes. The classifiers also achieved high precision, recall, and accuracy. This study supports a nationally agreed standard in pathology reporting and the use of text mining for encoding, classifying, and production of high-quality information abstractions for cancer prognosis and research.

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification.

Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

RADIOMICS BASED SINGLE AND MULTI-CLASS GLIOMA CLASSIFICATION USING SUPPORT VECTOR MACHINE VARIANTS

The common type of primary brain tumor is glioma. The mortality rate of glioma patients is high due to delayed diagnosis, incorrect grading and treatment planning. Traditionally, gliomas were classified into Low Grade (grade-I and grade-II) and High Grade (grade-III and grade-IV). However, World Health Organization has insisted to classify the grades into grade-I(G-I), grade II(G-II), grade III(G-III) and grade IV(G-IV) individually to aid the physicians in clinical decision-making. Although there are limited number of studies reported to differentiate individual grades, the classification accuracy was low. Consequently, in this work single-class (G-II vs. G-III, G-II vs. G-IV and G-III vs. G-IV) and multi-class (G-II vs. G-III+IV, G-III vs. G-II+IV and G-IV vs. G-II+III) analysis was performed using specific region of tumor and whole brain as Regions of Interest(ROI) by extracting radiomic features. The images for this study (N=75) were obtained from The Cancer Imaging Archive. Further, the statistically significant features were used in the classification of individual grades by implementing variants of Support Vector Machine (SVM) algorithm: SVM, Linear-SVM and Least-Squared SVM. Among these, Linear-SVM resulted in the highest classification accuracy (>80%) with average sensitivity, specificity and AUC values of >70%. The comparative analysis of whole brain versus tumor ROI showed that the latter yielded better classification accuracy.

Optimal feature selection in intrusion detection using SVM-CA

Feature selection plays a vital role in toning down the effects of the curse of dimensionality in the humungous datasets seen in intrusion detection. Feature selection algorithms are used to pick the relevant features and averts the extraneous and repeated features from the dataset to improve the efficiency. It can reduce processing time, dimension of data and enhance the performance of the system in terms of precision and training time. This paper proposes a novel variant of support vector machine, known as SVM correlation algorithm (SVM-CA) to choose the relevant features. The combination of SVM with correlation algorithm enhances the classification accuracy. Our proposed SVM-CA algorithm deals with the problems faced by the existing algorithm like low accuracy and high detection time. The performance of the algorithm is appraised by five parameters including the modelling time, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental results show that our proposed technique decreases the false positive rate and processing time.

D.C. programming for sparse proximal support vector machines

Proximal support vector machine (PSVM), as a variant of support vector machine (SVM), is to generate a pair of non-parallel hyperplanes for classification. Although PSVM is one of the powerful classification tools, its ability on feature selection is still weak. To overcome this defect, we introduce ℓ0-norm regularization in PSVM which enables PSVM to select important features and remove redundant features simultaneously for classification. This PSVM is called as a sparse proximal support vector machine (SPSVM). Due to the presence of ℓ0-norm, the resulting optimization problem of SPSVM is neither convex nor smooth and thus, is difficult to solve. In this paper, we introduce a continuous nonconvex function to approximate ℓ0-norm, and propose a novel difference of convex functions algorithms (DCA) to solve SPSVM. The main merit of the proposed method is that all subproblems are smooth and admit closed form solutions. The effectiveness of the proposed method is illustrated by theoretical analysis as well as some numerical experiments on both simulation datasets and real world datasets.

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code. In this work, we use distinctive pre-trained models (AlexNet and Inception-V3) for feature extraction. The purpose of deep convolutional neural network (CNN) feature extraction from two models is to improve the malware classifier accuracy, because both models have characteristics and qualities to extract different features. This technique produces a fusion of features to build a multimodal representation of malicious code that can be used to classify the grayscale images, separating the malware into 25 malware classes. The features that are extracted from malware images are then classified using different variants of support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and other classifiers. To improve the classification results, we also adopted data augmentation based on affine image transforms. The presented method is evaluated on a Malimg malware image dataset, achieving an accuracy of 99.3%, which makes it the best among the competing approaches.

A novel F-SVM based on FOA for improving SVM performance

Parameter setting is critical for the solution efficiency and accuracy of support vector machine (SVM). The general methods for setting parameters include Grid search method (GS) and some typical swarm intelligence algorithms. However, these SVM variants only consider the margin but ignore the radius. This paper develops a new radius-margin-based SVM model with fruit fly optimization algorithm (FOA) called FOA-F-SVM, which considers the maximization of margin and the minimization of radius information. The FOA is adopted to optimize the penalty factor and parameter of RBF in F-SVM. The established model is solved in three steps, including initialization of matrix, decision of hyperplane and solution of transformation matrix. The effectiveness of the proposed FOA-F-SVM is evaluated against eight UCI datasets and eight comparison algorithms. The performance of the FOA-F-SVM is validated using the experimental results, and it is observed that FOA-F-SVM algorithm can produce more appropriate model parameters and significantly reduce the computational cost, which generates a high classification accuracy.