Performance Of SVM Classifier Research Articles

A novel automatic detection and classification algorithm for elderly cognitive impairment using CSVM

Alzheimer’s disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely.

Feature Selection Using Firefly Algorithm With Tree-Based Classification In Software Defect Prediction

Defects that occur in software products are a universal occurrence. Software defect prediction is usually carried out to determine the performance, accuracy, precision and performance of the prediction model or method used in research, using various kinds of datasets. Software defect prediction is one of the Software Engineering studies that is of great concern to researchers. This research was conducted to determine the performance of tree-based classification algorithms including Decision Trees, Random Forests and Deep Forests without using feature selection and using firefly feature selection. And also know the tree-based classification algorithm with firefly feature selection which can provide better software defect prediction performance. The dataset used in this study is the ReLink dataset which consists of Apache, Safe and Zxing. Then the data is divided into testing data and training data with 10-fold cross validation. Then feature selection is performed using the Firefly Algorithm. Each ReLink dataset will be processed by each tree-based classification algorithm, namely Decision Tree, Random Forest and Deep Forest according to the results of the firefly feature selection. Performance evaluation uses the AUC value (Area under the ROC Curve). Research was conducted using google collab and the average AUC value generated by Firefly-Decision Tree is 0.66, the average AUC value generated by Firefly-Random Forest is 0.77, and the average AUC value generated by Firefly-Deep Forest is 0, 76. The results of this study indicate that the approach using the Firefly algorithm with Random Forest classification can work better in predicting software damage compared to other tree-based algorithms. In previous studies, tree-based classification with hyperparameter tuning on software defect prediction datasets obtained quite good results. In another study, the classification performance of SVM, Naïve Bayes and K-nearest neighbor with firefly feature selection resulted in improved performance. Therefore, this research was conducted to determine the performance of a tree-based algorithm using the firefly selection feature.

Metabolic–related gene signatures for survival prediction and immune cell subtypes associated with prognosis in intrahepatic cholangiocarcinoma

ObjectivesOur study aimed to reveal the metabolic-related gene signatures for survival prediction and immune cell subtypes associated with IHCC prognosis. MethodsDifferentially expressed metabolic genes were identified between survival group and dead group which were divided according to survival at discharge. Recursive feature elimination (RFE) and randomForest (RF) algorithms were applied to optimize the combination of feature metabolic genes, which were used to generate SVM classifier. Performance of SVM classifier was evaluated by receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was conducted to uncover the activated pathways in high risk group, and differences in immune cell distributions were revealed. ResultsThere were 143 differentially expressed metabolic gens. RFE and RF identified 21 overlapping differentially expressed metabolic genes, and the constructed SVM classifier had excellent accuracy in training and validation dataset. RS survival prediction model was consisted of 10 metabolic genes. RS model had reliable predictive capability in the training and validation dataset. GSEA revealed 15 significant KEGG pathways that were relatively activated in the high risk group. High risk group had obviously lower counts of B cell naive and T cell CD4+ memory resting, while higher counts of B cell plasma and macrophage M2. ConclusionPrognostic prediction model of 10 metabolic genes could accurately predict the prognosis of IHCC patients.

Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC’20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.

Neighborhood rough set based ensemble feature selection with cross-class sample granulation

Exploring feature significance associated with label is a fundamental task in the architecture of feature selection. Nevertheless, most of the existing schemes are limited by the global feature significance over the entire universe. It follows that some specific characteristics of features implied in sample subspaces may be overlapped. To fill such a gap, a novel ensemble feature selection with cross-class sample granulation is developed. Our method explicitly involves two main phases: (1) cross-class sample granulation — data is separated into multiple granules which are generated by querying the locations of samples in their respective classes, so as to provide local bases; (2) ensemble feature selection — localized evaluations of feature significance are integrated which are induced by leveraging multiple homogeneous fine-granularity measures from those bases, so as to select qualified features. To validate the effectiveness of our proposed method, it is compared with several well-established feature selection schemes in CART, KNN and SVM classification performance. Experimental results on 20 UCI data sets demonstrate that our method is superior as it yields higher accuracy with satisfactory elapsed time.

Design of Network Intrusion Detection Model Based on TCA

The traditional machine learning model cannot effectively identify the new network traffic data set, resulting in the model failure. Therefore, in this paper, by analyzing the problems of current network intrusion detection (NID) and combining the application of transfer theory in the detection model, a NID model based on transfer component analysis (TCA) is proposed. Among them, the specific mathematical derivation of the algorithm and the detection process of transfer model are introduced in detail. Then, the classification performance of KNN and SVM based on TCA algorithm for network abnormal traffic is compared. The results show that the TCA algorithm proposed in this paper can effectively improve the accuracy of NID, which is meaningful to expand the application scope of network abnormal traffic detection scheme based on machine learning.

Analysing the Sensitivity of SVM Kernels on Hyperspectral Imagery for Land Use Land Cover Classification

Support Vector Machine is a non- parametric statistical learning theory-based machine learning technique that classifies the data by maximising the margin between two classes by constructing a hyperplane between them. Although SVM’s are well known for binary classification problems, for multiclass classification problems- they use certain kernels, to turn the linear boundaries into non-linear boundaries in a higher dimensional feature space. In this context, the type of kernel used in non-linear SVMs have a profound effect on final performance of SVM classifier. The current study focuses on analysing the sensitivity of various linear and non-linear SVM kernels and parameters used in them for classifying 10 different land use/cover features from remotely sensed space borne hyperspectral image. Forty five different models were tested to analyse the performance of each kernel with varying penalty values for classification of land use/land cover classes. Analysis of the classified maps was made based on the final overall accuracy, error/penalty parameter and degree of polynomial. Experimental results show that SVM with RBF kernel outperformed the other kernels with an overall accuracy of 90.63%, with an error penalty of 100 and a gamma value of 0.006 followed by polynomial kernel of degree 2.

Weighted p-norm distance t kernel SVM classification algorithm based on improved polarization

The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM. To improve the applicability and classification prediction effect of SVM in different areas, in this paper, we propose a weighted p-norm distance t kernel SVM classification algorithm based on improved polarization. A t-class kernel function is constructed according to the t distribution probability density function, and its theoretical proof is presented. To find a suitable mapping space, the t-class kernel function is extended to the p-norm distance kernel. The training samples are obtained by stratified sampling, and the affinity matrix is redefined. The improved local kernel polarization is established to obtain the optimal kernel weights and kernel parameters so that different kernel functions are weighted combinations. The cumulative optimal performance rate is constructed to evaluate the overall classification performance of different kernel SVM algorithms, and the significant effects of different p-norms on the classification performance of SVM are verified by 10 times fivefold cross-validation statistical comparison tests. In most cases, the results using 6 real datasets show that compared with the traditional kernel function, the proposed weighted p-norm distance t kernel can improve the classification prediction performance of SVM.

A Fault Diagnosis Scheme Using Hurst Exponent for Metal Particle Faults in GIL/GIS

A diagnosis scheme using the Hurst exponent for metal particle faults in GIL/GIS is proposed to improve the accuracy of classification and identification. First, the diagnosis source signal is the vibration signal generated by the collision of metal particles in the electric field. Then, the signal is processed via variational mode decomposition (VMD) based on particle swarm optimization with adaptive parameter adjustment (APA-PSO). In the end, fault types are classified and identified by an SVM model, whose feature vector is composed of the Hurst exponents of each intrinsic mode function (IMF-H). Extensive experimental data verify the effect of this new scheme. The results exhibit that the classification performance of SVM is significantly improved by the new feature vector. Furthermore, the VMD based on APA-PSO with adaptive parameter adjustment can effectively enhance the decomposition quality.

Detection of Liver Disorder Using RBF SVM in Comparison with Naïve Bayes to Measure the Accuracy, Precision, Sensitivity and Specificity

Aim: Machine learning techniques are rapidly used in the area of medical research due to its impressive results in diagnosis and prediction of diseases. The objective of this study is to evaluate the performance of SVM classifier in identification of liver disorder by comparing it with Naive Bayes algorithm. Methods and Materials: A total of 31619 samples are collected from three liver disease datasets available in kaggle. These samples are divided into training dataset (n = 22133 [70%]) and test dataset (n = 9486 [30%]). Accuracy, precision, specificity and sensitivity values are calculated to quantify the performance of the SVM algorithm. Results: SVM achieved accuracy, precision, sensitivity and specificity of 73.64%, 97.82%, 97.56% and 69.77% respectively compared to 57.31%, 41.39%, 94.87% and 37.20% by Naive Bayes algorithm. Conclusion: In this study it is found that the RBF SVM algorithm performed better than the Naive Bayes algorithm in liver disorder detection of the datasets considered.

Research on Electricity Information Acquisition System Based on Sample Data Mining Model

In power user information collection and detection, power companies generally have a variety of different detection needs, or need to solve the problem while having additional requirements for certain aspects. Therefore, the SVM classification technology is used in the paper to carry out more detailed pattern recognition of power consumption characteristics for small-scale users or users with major suspicions. Moreover, given the imbalance of the abnormal electricity detection data set, a comprehensive processing model of unbalanced samples is constructed. Meanwhile, the differential evolution algorithm is applied to complete the SVM parameter optimization, which not only solves the problem that the SVM classification performance is significantly affected by the parameters, but also ensures the operating efficiency of the integrated classification model.

Bio-Inspired Algorithms for Optimal Feature Selection in Motor Imagery-Based Brain-Computer Interface.

Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.

EN YAKIN K KOMŞU VE DESTEK VEKTÖR MAKİNELERİ YÖNTEMLERİ KULLANILARAK MEME KANSER

Breast cancer, the most common type of cancer among women, affects millions of women every year. The aim of this study is to find properties for estimating breast cancer using two basic classifier methods and to compare the classifier performances. In the study, a ready data set including age, body mass index (BMI), glucose, resistin, insulin, homeostasis model assessment for insulin resistance (HOMA-IR), monocyte chemoattractant protein 1 (MCP1), leptin hormone and adiponectin hormone were used. 10 fold cross verification (10 fold) method was used while selecting the training and test data. Classification performance of SVM from classifiers was better than KNN. It was seen that the data set with 5 clinical features showed the best classification performance with 85.3% classification accuracy and 89.1% specificity (the rate of detecting those with cancer) values. It has been determined that the most suitable features for breast cancer prediction are age, BMI, resistin, glucose and adiponectin parameters.

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed.

Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms

Ipsilateral views of digital mammograms help radiologists to localize and confirm abnormal lesions during diagnosis of breast cancers. This study aims at developing algorithms which improve accuracy of computer-aided diagnosis (CADx) for analyzing breast abnormalities on ipsilateral views. The proposed system is a fusion of single and two view systems. Single view approach detects and characterizes suspicious lesions on craniocaudal (CC) and medio-lateral oblique (MLO) view separately using geometric and textural features. Lesions detected on each view are paired with potential lesions on another view. The proposed algorithm computes the correspondence score of each lesion pair. Single view information is fused with two views correspondence score to discriminate malignant tumours from benign masses using the SVM classifier. Performance of SVM classifier is assessed using five-fold cross validation (CV), Kappa metric and ROC analysis. Algorithms are applied to 110 pairs of mammograms from local dataset and 74 pairs from open dataset. Single view scheme yielded image-based sensitivity of 91.63% and 88.17% at 1.35 and 1.51 false positives per image (FPs/I) on local and open dataset respectively. Single view classification yielded FPs/I of 1.03 and 1.20 with sensitivity of 70%. Fusion based two views scheme using SVM classifier produced average case-based sensitivity of 75.91% at 0.69FPs/I and 73.65% at 0.72FPs/I on local and open dataset respectively. Fusion of single view features with two view correspondence score leads to improved case-based detection sensitivity. Proposed fusion based approach results into accurate and reliable diagnosis of breast abnormalities than single view approach.

Computational methods for recognition of cancer protein markers in saliva.

In recent years, many studies have supported that cancer tissues can make disease-specific changes in some salivary proteins through some mediators in the pathogenesis of systemic diseases. These salivary proteins have the potential to become cancer-specific biomarkers in the early diagnosis stage. How to effectively identify these potential markers is one of the challenging issues. In this paper, we propose novel machine learning methods for recognition cancer biomarkers in saliva by two stages. In the first stage, salivary secreted proteins are recognized which are considered as candidate biomarkers of cancers. We picked up 557 salivary secretory proteins from 20379 human proteins by public databases and published literatures. Then, we present a training set construction strategy to solve the imbalance problem in order to make the classification methods get better accuracy. From all human protein set, the proteins belonging to the same families as salivary secretory proteins are removed. After that, we use SVC-KM method to cluster the remaining proteins, and select negative samples from each cluster in proportion. Next, the features of proteins are calculated by tools. We collect 24 protein properties such as sequence, structure and physicochemical properties, a total of 1087 features. An innovative procedure based on the local samples is proposed for selecting the appropriate features, in order to further improve the performance of SVM classifier. Experimental results show that the average sensitivity, specificity and accuracy of salivary secretory protein recognition using selected 32 features in training set are 97.09%, 98.10%, 97.61%, respectively. The use of these methods can improve the accuracy of recognition by solving the problems of unbalanced sample size and uneven distribution in training set. In the second stage, we apply the best model to dig out the salivary secreted proteins from 58 reported cancer markers, and get a total of 42 proteins which are considered to be used for salivary diagnosis. We analyze the gene expression data of three types of cancer, and predict that 33 genes will appear in saliva after they are translated into proteins. This study provides an important computational tool to help biologists and researchers reduce the number of candidate proteins and the cost of research. So as to further accelerate the discovery of cancer biomarkers in saliva and promote the development of saliva diagnosis.

MBO-SVM-based exudate classification in fundus retinal images of diabetic patients

Exudates act as early indications of diabetic retinopathy which may lead to blindness among diabetic patients. The recognition of exudates with a high accuracy and sensitivity is an important and challenging diagnostic task. In the framework of computer assisted diagnosis of diabetic retinopathy, a novel hybrid algorithm using Migrating Bird Optimisation and Support Vector Machine (MB-SVM) classifiers has been proposed in this paper. Initially, the preprocessing is performed with Gaussian filter to eradicate the noisy intensities and background pixels from the given fundus retinal image. We have detected the optic disc by means of Circular Hough Transform and eliminated it using Otsu thresholding algorithm and Gray scale dilation with flat disc structuring. The blood vessels are extracted using thresholding algorithm and then edge detection is performed by using kirsch’s template. After heuristic experimentation, it is evident that the performance of SVM classifier can be improved by concurrently optimising its parameters using MBO algorithm. The hybrid supervised model of MB-SVM has been employed to classify the fundus retinal images into normal and abnormal classes. The images that are classified as abnormal will be further categorised into soft, moderate and severe sub-classes. Experimental validation on a publicly available STARE data-set demonstrates the improved performance of the proposed method over existing methods.

Research on grounding grid corrosion classification method based on convolutional neural network

Aiming at the problem that the traditional detection methods can not accurately classify the corrosion degree of grounding grids. The corrosion image is taken as the research object, the convolution neural network is used as the algorithm firstly to classify the corrosion degree. Firstly, the corrosion simulation experiment was carried out, and the sample library was established by using the corrosion image collected in different stages. Then, according to the LeNet-5 model, the traditional CNN and improved CNN models were designed for corrosion classification of grounding grid. Simulation experiments were carried out in the preprocessed samples. Finally, the experimental results of Soft-max and SVM classifier are compared and analyzed. The results show: the classification results of the two models were better than those of the original samples, and the classification performance of SVM is better than that of Soft-max. The improved model can improve classification accuracy. This study fills the blank of detecting the corrosion degree of grounding grid by image method, and it is significant to quickly grasp the corrosion degree to avoid faults or accidents.

ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications

Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.

An accurate traffic classification model based on support vector machines

Network traffic classification is a fundamental research topic on high-performance network protocol design and network operation management. Compared with other state-of-the-art studies done on the network traffic classification, machine learning ML methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines SVMs based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real-time accurate SVM training model named SPP-SVM is proposed. An SPP-SVM is deducted from the scaling dataset and employs principal component analysis PCA to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP-SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP-SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two- and multi-class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP-SVM's two- and multi-class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.