Classification Accuracy Of Support Vector Machine Research Articles

Utilization of Filter Feature Selection with Support Vector Machine for Tumours Classification

Due to rapid technology advancement, machine learning has been widely used for solving cancer classification problem. Classification performance is highly depending on the quality of input features. With an explosive increase number of features of high dimensional data, the occurrence of ambiguous samples and data redundancy directly leads to poor classification accuracy. Therefore, this paper presents a utilization of filter feature selection using four filter methods such as Information Gain, Gain Ratio, Chi-Squared and Relief-F by performing attribute rankings to remove the irrelevant and redundant features and evaluate the significance and correlation of input data. Then, the classification will be performed using Support Vector Machine (SVM) to measure the accuracy performance based on the number of selected features. The performance measurement will be validated on standard Breast Cancer datasets consisting of 286 instances obtained from the UCI repository. Evaluation metrics such as accuracy, sensitivity, specificity and Area under Receiver Operating Characteristic Curve (AUC) will be used to assess the performance of the SVM classifier using four different filter methods. Experimental result shows that Gain ratio improves the accuracy of SVM classification compared to Information Gain, Chi-Squared and Relief-F in classifying breast cancer data with only small number of features selected.

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment.

Driving style is a very important indicator and a crucial measurement of a driver's performance and ability to drive in a safe and protective manner. A dangerous driving style would possibly result in dangerous behaviors. If the driving styles can be recognized by some appropriate classification methods, much attention could be paid to the drivers with dangerous driving styles. The driving style recognition module can be integrated into the advanced driving assistance system (ADAS), which integrates different modules to improve driving automation, safety and comfort, and then the driving safety could be enhanced by pre-warning the drivers or adjusting the vehicle's controlling parameters when the dangerous driving style is detected. In most previous studies, driver's questionnaire data and vehicle's objective driving data were utilized to recognize driving styles. And promising results were obtained. However, these methods were indirect or subjective in driving style evaluation. In this paper a method based on objective driving data and electroencephalography (EEG) data was presented to classify driving styles. A simulated driving system was constructed and the EEG data and the objective driving data were collected synchronously during the simulated driving. The driving style of each participant was classified by clustering the driving data via K-means. Then the EEG data was denoised and the amplitude and the Power Spectral Density (PSD) of four frequency bands were extracted as the EEG features by Fast Fourier transform and Welch. Finally, the EEG features, combined with the classification results of the driving data were used to train a Support Vector Machine (SVM) model and a leave-one-subject-out cross validation was utilized to evaluate the performance. The SVM classification accuracy was about 80.0%. Conservative drivers showed higher PSDs in the parietal and occipital areas in the alpha and beta bands, aggressive drivers showed higher PSD in the temporal area in the delta and theta bands. These results imply that different driving styles were related with different driving strategies and mental states and suggest the feasibility of driving style recognition from EEG patterns.

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.

While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors. The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention. A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively. A machine learning scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a scheme can be integrated into clinical decision support systems to optimize tumor classification.

Mental Tasks EEG Signal Classification Using Support Vector Machine

This paper presents a result of electroencephalography (EEG) signal classification for mental tasks such as thinking forward, backward, left, and right. The EEG data in this study were recorded from Emotive device with 14 channels and 2 references. The aim of this study is to identify the most sensitive channels to the mental task classification. Prior to feature extraction, the EEG signal were decomposed using wavelet with three level decomposition. Eighteen features were extracted from the processed data. Principal component analysis (PCA) is then used to reduce 18 features into 3 principal components. The principal component were classified using support vector machine (SVM). The results show that the SVM classification accuracy of 75%.

Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine.

Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, prostate, bone marrow, bladder, and air. Twenty-two men with a mean age of 30±14 years were included in this prospective study. The images were acquired with a 3 Tesla MRI scanner. An inversion recovery-prepared turbo spin echo sequence was used to obtain T1-weighted images at different inversion times with a TR of 14000 ms. A 32-echo spin echo sequence was used to obtain the T2-weighted images at different echo times with a TR of 5000 ms. T1 and T2 relaxation times, synthetic T1- and T2-weighted images and anatomical probabilistic maps were calculated and used as input features of a SVM for segmenting and classifying tissues within the pelvic region. The mean SVM classification accuracy across subjects was calculated for the different tissues: prostate (94.2%), fat (96.9%), muscle (95.8%), bone marrow (91%) and bladder (82.1%) indicating an excellent classification performance. However, the segmentation and classification for air (within the rectum) may not always be successful (mean SVM accuracy 47.5%) due to the lack of air data in the training and testing sets. Our findings suggest that SVM can reliably segment and classify tissues in the pelvic region.

Improving the SVM gender classification accuracy using clustering and incremental learning

AbstractGender recognition has been playing a very important role in various applications such as human–computer interaction, surveillance, and security. Nonlinear support vector machines (SVMs) were investigated for the identification of gender using the Face Recognition Technology (FERET) image face database. It was shown that SVM classifiers outperform the traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, and nearest neighbour). In this context, this paper aims to improve the SVM classification accuracy in the gender classification system and propose new models for a better performance. We have evaluated different SVM learning algorithms; the SVM‐radial basis function with a 5% outlier fraction outperformed other SVM classifiers. We have examined the effectiveness of different feature selection methods. AdaBoost performs better than the other feature selection methods in selecting the most discriminating features. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, whereas method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods have increased the classification accuracy.

A Classification Prediction Analysis of Flight Cancellation Based on Spark

Nowadays the phenomenon of flight delays and cancellations is becoming more and more serious. Flight delays and cancellations not only waste transportation resources, but also affect passengers’ travel plans, which cause an increase in passenger discontent and complaint rates[2]. The passengers’ dissatisfaction and distrust of airlines seriously damage the airlines’ corporate reputation and then affect passengers’ loyalty. Therefore, based on the information of 5 million flight data in the United States in 2016, we predicted the flight cancellations based on four classification algorithms: logistics regression, support vector machine (SVM), naive bayes and decision tree. We compared the training time of these algorithms by changing the number of nodes in the Spark cluster[9], and we also compared the classification accuracy, AUC (Area Under Curve) and PR(Precision-Recall). Experimental results show that: (1) When the number of nodes was 1, the average training time of these algorithms was 460 seconds. When the number of nodes was 3,5,7,9, the corresponding average training time were 207.25 seconds, 89.75 seconds, 44 seconds, 42 seconds, and when the number of nodes was 11, the average training time was 43.5 seconds; (2) Starting from 7 nodes, as the number of nodes increased while the decline rate of the average training time slowed down; (3) When the number of nodes was 1 and 3, the training time of naive bayes was the shortest, which were 386 seconds and 185 seconds respectively. When the number of nodes was 5,7,9 and11, the training time of SVM was the shortest, which were 72 seconds, 21seconds, 19 seconds and 18 seconds respectively; (4) the classification accuracy of SVM and decision tree was almost 90%, but naive bayes was just about 50.8% and logistics regression was only about 62.4%; (5) Both the AUC and PR of the decision tree algorithm were the highest: AUC was 0.558 and PR was 0.439. Therefore, the decision tree algorithm was the most suitable for predicting whether the flight would be cancelled or not, and there was a 90% probability that the prediction would be successful.

Song of my people: dialect differences among sympatric social groups of short-finned pilot whales in Hawai’i

In many social species, acoustic dialects are used to differentiate among social groups within a local population. These acoustic dialects and their corresponding social groups are often related to distinct foraging behaviors or spatial movement patterns, and it is possible that vocal repertoire variability is one of the proximate mechanisms driving or maintaining genetic and ecological diversity at a subspecies level in social species. Short-finned pilot whales (Globicephala macrorynchus) inhabiting Hawaiian waters have a stable hierarchical social structure, with familial social units associating in larger social clusters within island-associated communities. In this study, we test the hypothesis that sympatric social groups of short-finned pilot whales have acoustically differentiated dialects, which may be used to maintain the social structure. We first examined call composition of social calls collected from photographically identified social clusters of short-finned pilot whales around the Main Hawaiian Islands, using a catalog of manually classified calls, and found that call composition differed among clusters. We then conducted ANOVA and support vector machine (SVM) learning analyses of the acoustic features of social calls. Social clusters were significantly differentiated in their acoustic features, and the SVM classification accuracy was 60%. These results indicate that vocal repertoire reflects social segregation in short-finned pilot whales and may be a driving mechanism of differentiation, potentially contributing to genetic diversity within populations. This suggests divergent acoustic population structure; however, the small sample size in this study decreases the ability to detect acoustic differences among groups. Additional sampling will improve our power to detect acoustic differences among social clusters of Hawaiian pilot whales and improve classification accuracy. The pattern described here highlights the importance of increasing the spatial and temporal resolution of conservation and management plans for this species, in order to conserve subpopulation genetic and social structure. In some species, vocal repertoires differ among social groups or populations of a species that use the same habitat. These differences, called dialects, are thought to be important to maintaining segregation among groups of animals with overlapping distributions, and in some cases may increase intra-specific ecological or genetic variability. This study is the first to provide evidence that sympatric social clusters of short-finned pilot whales have different vocal repertoires, and that vocal repertoire within groups may change with behavioral context. In terrestrial (e.g., elephants) and marine (e.g., killer whales, sperm whales) species with similarly stable social hierarchies, where acoustic dialects, genetic diversity, and/or ecological variability are linked with social structure, anthropogenic stressors have precipitated rapid declines in abundance with slow or nonexistent recovery. Given the myriad threats faced by short-finned pilot whales in the Hawaiian Islands, including fisheries bycatch, military and commercial anthropogenic noise, and vessel strikes, understanding intra-population social structure and its links with genetic structure and ecological variability is imperative to the proper conservation and management of this species.

Parameters Optimization of SVM using the Dual Population ACO

The support vector machine (SVM) parameters optimization of previous lacks of theoretical guidance. The algorithm is easy to fall into local optimum. The dual population ant colony algorithm is helpful to SVM parameters optimization in ability. The independent solution and exchange of two population information and the change of pheromone can avoid the stagnation of the algorithm or get into local optimum, and find the global optimal solution. The accuracy of SVM classification is used as the objective function. And the experimental results show that the algorithm improves the accuracy of the classification of support vector machine parameters.

Dropout training for SVMs with data augmentation

Dropout and other feature noising schemes have shown promise in controlling over-fitting by artificially corrupting the training data. Though extensive studies have been performed for generalized linear models, little has been done for support vector machines (SVMs), one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a reweighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we consider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected hinge loss and the nonlinearity of latent representations. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.

Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster.

Autism spectrum disorder (ASD) is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM) are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus, we used multiple SVMs to classify ASD patients and typical controls (TC). Resting-state functional magnetic resonance imaging (fMRI) data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2 mm or ±2°. Then the random SVM cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (IFG) (orbital and opercula part), hippocampus, and precuneus. It is indicated that the method of random SVM cluster may apply to the auxiliary diagnosis of ASD.

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi’ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

A Multiple Kernel Learning Model Based on p-Norm.

By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L1-norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the Lp-norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of Lp-norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL (L1- and L2-norms based GMKL) method, in which L1 L2 established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the p-norm, by joining L1- and Lp-norms. Consequently, the L1- and L2-norms based GMKL is a special case in our method when p = 2. Experiments demonstrated that our L1- and Lp-norms based MKL offers a higher accuracy than the L1- and L2-norms based GMKL in the classification, while keeping the properties of the L1- and L2-norms based on GMKL.

Exploring feature dimensionality reduction methods for enhancing automatic sport image annotation

Nowadays, multimedia information requires the demand to investigate and apply efficient techniques for better annotation and retrieval purposes. In the content-based indexing, low-level features are generally extracted from images to serve as image descriptors. Other than the descriptor poses a computational overhead, the learning model may also tend to overfit, resulting in performance degeneration. This work solves such problems in the sport image domain by proposing feature dimensionality reduction techniques for the retrieval and annotation of image datasets. Different techniques are investigated, such as Information Gain, Gain Ratio, Chi-Square, and Latent Semantic Analysis (LSA), and applied for sport images classification using Support Vector Machine (SVM) classifier. A comparison between the performances of applying SVM alone and when incorporating the different reduction methods is presented. Experimental results show that the SVM classification accuracy is 76.4%; while integrating LSA technique manages to raise the accuracy to 96%, with the other techniques recording 74% accuracy at 50% feature space reduction.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used. The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95. The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95% and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6% and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9% and f-measure=0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

Anomaly Intrusion Detection Based on A hybrid GSA-SVM Classifier

Support vector machine (SVM) is commonly used in IDSs because of its robustness and efficiency in the network classification. However, Parameter optimization influences the classification accuracy of SVM significantly. In order to improve, a hybrid classifier is designed based on a combination of the GSA and SVM algorithms to optimize the accuracy of the SVM classifier. In the GSASVM classifier, the GSA guides the selection of potential subsets that lead to the best detection accuracy. The GSA-SVM algorithm evaluated using KDD CUP 99 data set and compared to the outperformance of the original SVM algorithms. The results show that the performance of GSA-SVM algorithm has higher detection rate with lower false positive rate.
 Keywords: Network Intrusion Detection, Gravitational Search Algorithm (GSA), support vector machines (SVM)

Assessment of elbow spasticity with surface electromyography and mechanomyography based on support vector machine.

The Modified Ashworth Scale (MAS) is the gold standard in clinical for grading spasticity. However, its results greatly depend on the physician evaluations and are subjective. In this study, we investigated the feasibility of using support vector machine (SVM) to objectively assess elbow spasticity based on both surface electromyography (sEMG) and mechanomyography (MMG). sEMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were recorded simultaneously on patients' biceps and triceps when they extended or bended elbow passively. 39 post-stroke patients participated in the study, and were divided into four groups regarding MAS level (MAS=0, 1, 1+ or 2). The three types of features, root mean square (RMS), mean power frequency (MPF), and median frequency (MF), were calculated from sEMG and MMG signal recordings. Spearman correlation analysis was used to investigate the relationship between the features and spasticity grades. The results showed that the correlation between MAS and each of the five features (MMG-RMS of the biceps, MMG-RMS of the triceps, the EMG-RMS of the biceps, EMG-RMS of the triceps, EMG-MPF of the triceps) was significant (p<;0.05). The four spasticity grades were identified with SVM, and the classification accuracy of SVM with sEMG, MMG, sEMG-MMG were 70.9%, 83.3%, 91.7%, respectively. Our results suggest that using the SVM-based method with sEMG and MMG to assess elbow spasticity would be suitable for clinical management of spasticity.