Online Support Vector Machine Research Articles

An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

Online monitoring of social networks offers exciting features for platforms, enabling both technical and behavioral analysis. Numerous studies have explored the adaptation of traditional quality control methods for detecting change points within social networks. However, the current research studies face limitations such as an overreliance on case-based attributes, high computational costs, poor scalability with large networks, and low sensitivity in fast change point detection. This paper proposes a novel algorithm for social network monitoring using One-Class Support Vector Machines (OC-SVMs) to address these limitations. Additionally, using both nodal and network-level attributes makes it versatile for diverse social network applications and effectively detecting network disturbances. The algorithm utilizes a well-defined training data dictionary with an updating procedure for evolutionary networks, enhancing memory and time efficiency by reducing the processing of input data. Extensive numerical experiments are conducted using an EpiCNet model to simulate interactions in an online social network, covering six change scenarios to evaluate the proposed methodology. The results show lower Average Run Length (ARL) and Expected Delay Detection (EDD), demonstrating the superior accuracy and effectiveness of the OC-SVM algorithm compared to alternative methods. Applying OC-SVM to the Enron Email network indicates its capability to identify change points, reflecting the tumultuous timeline that led to Enron's downfall. This further validates the substantial advancement of OC-SVM in social network monitoring and opens doors to broader real-world applications.

Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization.

Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation method is proposed for the fully automated grading of brain tumors in magnetic resonance (MR) images. First, the tumor is segmented based on two characteristics of the tumor appearance (intensity and edges information). Second, the features of the tumor region are extracted. Then, the online support vector machine with the kernel (OSVMK) by dynamic fuzzy rule-based optimization of the parameters is used for the grading of tumors. The performance evaluation of the proposed tumor segmentation method was performed by manual segmentation using similarity criteria. Also, tumor grading results compared the proposed online method, the conventional online method, and the batch SVM with the kernel (batch SVMK) in terms of accuracy, precision, recall, specificity, and execution times. The segmentation results show a good correlation between the tumor segmented by the proposed method and by experts manually. Also, the grading results based on the accuracy, precision, recall, and specificity, 95.20%, 97.87%, 96.48%, and 96.45%, respectively, indicate the acceptable performance of the proposed method. The execution times of the introduced online method are much less than the batch SVMK. The method demonstrates the potential of fully automated tumor grading to provide a non-invasive diagnosis in order to determine the treatment strategy for the disease. So the physicians, according to the tumor's grade, can match the treatment of the brain tumor to the patient's individual needs and thus make the best course of treatment for each patient.

Online Support Vector Machine with a Single Pass for Streaming Data

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement.

An Information Granulated Based SVM Approach for Anomaly Detection of Main Transformers in Nuclear Power Plants

The main transformer is critical equipment for economically generating electricity in nuclear power plants (NPPs). Dissolved gas analysis (DGA) is an effective means of monitoring the transformer condition, and its parameters can reflect the transformer operating condition. This study introduces a framework for main transformer predictive-based maintenance management. A condition prediction method based on the online support vector machine (SVM) regression model is proposed, with the input data being preprocessed using the information granulation method, and the parameters of the model are optimized using the particle swarm optimization (PSO) algorithm. Using DGA data from the NPP data acquisition system, two experiments are designed to verify the trend tracing and prediction envelope ability of main transformers installed in NPPs with different operating ages of the proposed model. Finally, how to use this framework to benefit the maintenance plan of the main transformer is summarized.

Weighted Incremental–Decremental Support Vector Machines for concept drift with shifting window

We study the problem of learning the data samples’ distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Machines (SVMs) can learn weighted input instances and that they can also be trained online (incremental–decremental learning). Combining these two SVM properties, the open problem is to define an online SVM concept drift model with shifting weighted window. The classic SVM model should be retrained from scratch after each window shift. We introduce the Weighted Incremental–Decremental SVM (WIDSVM), a generalization of the incremental–decremental SVM for shifting windows. WIDSVM is capable of learning from data streams with concept drift, using the weighted shifting window technique. The soft margin constrained optimization problem imposed on the shifting window is reduced to an incremental–decremental SVM. At each window shift, we determine the exact conditions for vector migration during the incremental–decremental process. We perform experiments on artificial and real-world concept drift datasets; they show that the classification accuracy of WIDSVM significantly improves compared to a SVM with no shifting window. The WIDSVM training phase is fast, since it does not retrain from scratch after each window shift.

Concept Drift Adaptation with Incremental–Decremental SVM

Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a model that adjusts the shifting window width using the Hoeffding statistical test. We evaluate AIDSVM performance on both synthetic and real-world drift datasets. Experiments show a significant accuracy improvement when encountering concept drift, compared with similar drift detection models defined in the literature. The AIDSVM is efficient, since it is not retrained from scratch after the shifting window slides.

A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

Fiber optic gyroscope (FOG) inertial measurement unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in positioning and navigation of military and aerospace fields, due to its simple structure, small size, and high accuracy. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. In order to reduce the FOG drift and improve the navigation accuracy, a long short-term memory recurrent neural network (LSTM-RNN) model is established, and a real-time acquisition method of the temperature change rate based on moving average is proposed. In addition, for comparative analysis, backpropagation (BP) neural network model, CART-Bagging, classification and regression tree (CART) model, and online support vector machine regression (Online-SVR) model are established to filter FOG outputs. Numerical simulation based on field test data in the range of -20°C to 50°C is employed to verify the effectiveness and superiority of the LSTM-RNN model. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. This proposed solution can be applied in military and aerospace fields.

Online learning method based on support vector machine for metallographic image segmentation

The shape, size and distribution of the microstructure could certainly reveal mechanical properties. Therefore, it is important to segment the microstructure accurately. However, in view of the randomness in the metallographic detection period, a fixed segmentation algorithm with good performance in specific and known dataset may lead to wrong result when external condition changes. To solve the problem, we proposes an online learning pipeline to adjust the model adaptively by dynamic samples. First, a preprocessing method is deployed to smooth the uneven gray level. Next, local features and abstract features are extracted by a genetic algorithm-optimized boundary detector and a deep learning model, respectively. Then, online support vector machine is employed to update the model parameters under complex and changeable conditions in real time. Finally, a post-processing method is employed to get final result. A variety of experiments are presented to verify the effectiveness and the convergence of online algorithm. The experiment results indicate that the proposed pipeline can effectively extract local and abstract features, and the real-time updating model according to dynamic samples achieves state-of-the-art segmentation performance.

Machine learning as ecology

Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms—including support vector machines (SVMs)—have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning.

Scalable kernel convex hull online support vector machine for intelligent network traffic classification

Online support vector machine (SVM) is an effective learning method in real-time network traffic classification tasks. However, due to its geometric characteristics, the traditional online SVMs are sensitive to noise and class imbalance. In this paper, a scalable kernel convex hull online SVM called SKCHO-SVM is proposed to solve this problem. SKCHO-SVM involves two stages: (1) offline leaning stage, in which the noise points are deleted and initial pin-SVM classifier is built; (2) online updating stage, in which the classifier is updated with newly arrived data points, while carrying out the classification task. The noise deleting strategy and pinball loss function ensure SKCHO-SVM insensitive to noise data flows. Based on the scalable kernel convex hull, a small amount of convex hull vertices are dynamically selected as the training data points in each class, and the obtained scalable kernel convex hull can relieve class imbalance. Theoretical analysis and numerical experiments show that SKCHO-SVM has the distinctive ability of training time and classification performance.

Reliability prediction of further transit service based on support vector machine

The requirement for transit reliability grows with the increase of pace of life since unstable bus arrivals can raise the anxiety of waiting passengers. This paper proposes a reliability assessment method to evaluate the reliability of each bus stop on the route and the reliability of bus routes. In reliability prediction, the prediction target is locked by rolling horizon to reduce the interference of other information. In addition, a prediction method of the reliability of further transit service using the accurate online support vector machine is proposed. This prediction can provide more accurate and stable data for the arrival of buses and reduce unnecessary waiting of passengers. Finally, the reliability prediction method proposed is tested with the real data of a bus route in Dalian, China. The results show that the accurate online support vector machine with reasonable parameters can predict the reliability of transit service accurately.

An accelerator for online SVM based on the fixed-size KKT window

Support vector machine (SVM), as a general and useful supervised learning tool, is facing with some challenges such as low learning efficiency, poor generalization performance, noise sensitivity, etc. when it is applied to online learning tasks. To overcome these limitations, an accelerator model based on window technology and the KKT conditions for online SVM learning is proposed in this paper. The proposed model is not an independent online algorithm but may be regarded as an accelerator for other online SVM learning algorithms, and it constructs working set of SVM by a fixed-size window with the samples which violate the KKT conditions. The relationship between Lagrangain multipliers in dual problem of SVM and KKT conditions are analyzed in the case of online learning. On this basis, a fixed-size KKT window can be constructed according to whether the samples violate KKT conditions or not. Then, it takes the samples that violate the KKT conditions as the training window, which not only makes the training samples with the same size each time, but also ensures that all samples are useful for the hyperplane updating (it means that the classifier can be updated more smoothly). Two typical and specific online SVM algorithms are used as baseline, and the corresponding speeding online SVM learning algorithms with ”X+accelerator” models are proposed to testing the performance of the proposed accelerator. Comprehensive experiments clearly show that the proposed model can accelerate the online learning process effectively and has good robustness and generalization performance.

Research on Quality Evaluation of Online Reservation Hotel APP Based on a RBF Neural Network and Support Vector Machine

In order to evaluate the quality of online reservation hotel APP, RBF neural and support vector machine are used to evaluate the quality of online reservation hotel APP. First, the basic theory of the RBF neural network is studied, and the training algorithm of the RBF neural network is designed. Second, the basic model of support vector machine is analyzed, and the training algorithm is designed. Third, the evaluation index system of online reservation hotel APP is designed, and the weight of every index is established based on questionnaires and expert interview, and the evaluation simulation is carried out for 25 online reservation hotel APP, results show that the RBF neural network and support vector machine can obtain consistent evaluation results, and the support vector machine has better evaluation performance.

Fast CU partition-based machine learning approach for reducing HEVC complexity

With the development of video coding technology, the high efficiency video coding (HEVC) provides better coding efficiency compared to its predecessors H.264/AVC. HEVC improves rate distortion (RD) performance significantly with increased encoding complexity. Due to the adoption of a large variety of coding unit (CU) sizes, at RD optimization level, the quadtree partition of the CU consumes a large proportion of the encoding complexity. Hence, the computational complexity cost remains a critical issue that must be properly considered in the optimization task. In this paper, two machine learning-based fast CU partition method for inter-mode HEVC are proposed, to optimize the complexity allocation at CU level. First, we propose an online support vector machine (SVM)-based fast CU algorithm for reducing HEVC complexity. The later was trained in an online way. Second, a deep convolutional neural network (CNN) is designed to predict the CU partition, in which large-scale training database including substantial CU partition data is considered. Experimental results demonstrate that the proposed online SVM can achieve a time saving of 52.28% with a degradation of 1.928% in the bitrate (BR). However, the proposed deep CNN can reduce the encoding time by 53.99% with 0.195% BR degradation. Compared to the state-of-the art, the two proposed approaches outperform the related works in terms of both RD performance and complexity reduction at inter-mode.

A Hyper Meta-Heuristic Cascaded Support Vector Machines for Big Data Cyber-Security

At an incredible speed, cyber security evolves in the ever-changing setting of attacks. Organisation processing of information inward and outward is huge in quantity and determining a threat amidst of information is challengeable. Late discovery of such instance is standstill challenge of the meticulous process. Thence, detection of intrusion and its prevention are rising challenge in Big data factors. the information inundation generally incorporate the Big data terms to dataset. The majorly focused issues are industrial oriented in big data challenge. Existing systems for big data cyber security problems are based on Online Support Vector Machines (OSVMs) framework. Bi-objective optimisation problem with primary objectives is designed as OSVMs configuration process for improving accuracy and less complexity of model. Here, a bi-objective optimization is implemented based on an Artificial Bee Colony (ABC). However, Online Support Vector Machines (OSVMs) has issue with computational complexity, and prematurity and local optimum is major problems in ABC algorithm. By overcoming this issue, developed research system designs an Ensemble Support Vector Machine (ESVM) framework for big data cyber security. Initially, the feature selection is done by using improved K-means clustering. Based on the selected features the intrusion detection and malware detection are performed using ESVM approach. In this proposed research work, a bi-objective optimization problem is designed as the ESVM configuration process for improving accuracy and less complexity of model and achieve its objectives. Cuckoo Search (CS) optimization algorithm is implemented for the bi-objective optimization. accuracy, precision, recall and f-measure are the parametric meters compared in proposed research attaining higher performance against existing approaches.

Smooth iteration online support tension machine algorithm and application in fault diagnosis of electric vehicle extended range

It is difficult to develop accurate mathematical models to describe the range extender electric vehicles due to the non-linear and complex coupling of the monitoring signal sources resulted from the massive moving parts and complex architecture in range extender and the limited storage space of the diagnostic device. In this study, we proposed the smooth iterative online support tensor machine algorithm, which is combined with support higher-order tensor machine and online stochastic gradient descent method, and applied it to the fault diagnosis of the range indicator. Four methods with different algorithms, support vector machine, smooth iterative online support vector machine, linear support higher-order tensor machine, and smooth iterative online support tensor machine algorithms, were adopted to diagnose and classify the fault samples of the range indicator by comparing the diagnostic accuracy and model learning time. It is found that the fault diagnosis method based on the smooth iterative online tensor machine showed a higher accuracy, shorter learning time, and less storage space. Based on the experimental results, it is feasible to apply smooth iterative online support tensor machine model to the fault diagnosis of electric vehicle extenders.

An online support vector machine for the open-ended environment

Support Vector Machine (SVM) has been widely applied in expert systems. Traditional SVM works as a closed system, which means the parameters of the SVM freeze after training is finished. This strategy leads to the SVM cannot adapt to the online learning environment and also hinders the application of the SVM in expert systems which need to be updated in an online way. Many online SVMs retrain SVM with the current support vectors and new incoming data. However, the retained system usually shows a declined performance due to losing much information about the data. To handle this problem, we propose an Online Support Vector Machine (OSVM). The OSVM includes a Representative Prototype Area (RPA), which retains the representative data of all historical data. In the RPA, each class is maintained by an Online Incremental Feature Map (OIM) which learns a suitable representative set from the stream data automatically, e.g., we do not need to predefine a compression ratio, the OIM will dynamically adapt to the current input data. Retraining is periodically conducted on the updated OIM, which can avoid a declined performance and significantly reduce the computational cost. Variety data sets from different fields are used to test our method and the experimental results demonstrate that the accuracy of OSVM is comparable to that of the state-of-the-art SVM and the speed of OSVM is much faster. Moreover, OSVM is more stable than the other methods in the open-ended learning environment, where stream data from novel classes occur. This implies the OSVM has a good application prospect in online learning expert systems.

New SV selection strategy and local–global regularisation method for improving online SVM learning

During the online learning process of support vector machines (SVMs), when a newly added sample is violating the Karush–Kuhn–Tucker) conditions, the new sample should be a new SV and transfer the old samples between the SVs and the non-SVs. Normally, the performance of an SVM model is decided by the SVs, and the model should be updated by the newly added SVs; therefore, the selection of high-quality candidate SVs will lead to a better learning accuracy, whereas low-quality candidate SVs may result in low learning efficiency and unnecessary updating. A new strategy is proposed to select the candidate SVs. SVs are selected according to two new criteria: the importance and the informativeness criteria. Furthermore, a mixed local–global regularisation method is applied during the online learning process to improve the penalty coefficients. Experiment results show that the proposed algorithm can achieve a better performance with a faster speed and a higher accuracy when compared with traditional methods.

Online Robust Lagrangian Support Vector Machine against Adversarial Attack

In adversarial environment such as intrusion detection and spam filtering, the adversary-intruder or spam advertiser may attempt to produce contaminate training instance and manipulate the learning of classifier. In order to keep good classification performance, many robuster learning methods have been proposed to deal with the adversarial attack. Support Vector Machines(SVMs) is a kind of successful approach in the adversarial classification tasks and the investigation of robust SVMs is very popular. However, in many real application, the data including stain instance is coming dynamically. Batch learning which needs retraining when encountering new samples, will consume more computing resources. In this paper, we propose a robust Lagrangian support vector machine (RLSVM) with modified kernel matrix and explore the online learning algorithm on it. The experimental results show the robustness of RLSVM against label noise produced by adversaries under the online adversarial environment.

Robust Human Activity Recognition Using Smartphone Sensors via CT-PCA and Online SVM

Human activity recognition using either wearable devices or smartphones can benefit various applications including healthcare, fitness, smart home, etc. Instead of using wearable devices which are intrusive and require extra cost, we shall leverage on modern smartphones embedded with a variety of sensors. Due to the flexibility of using smartphones, the recognition accuracy will degrade with orientation, placement, and subject variations. In this paper, we propose a robust human activity recognition system in terms of orientation, placement, and subject variations based on coordinate transformation and principal component analysis (CT-PCA) and online support vector machine (OSVM). The proposed CT-PCA scheme is utilized to eliminate the effect of orientation variations. Experiments show that the proposed scheme significantly improves the activity recognition accuracy and outperforms the state-of-the-art methods on leave one orientation out experiments, which demonstrates the generalization ability of the proposed scheme on the data from unseen orientations. We also show the effectiveness of this scheme on placement and subject variations. However, the inherent difference of signal properties for different placement and subject dramatically reduces the recognition accuracy, especially for different placement. Thus, we present an efficient OSVM algorithm, that is, online-independent support vector machine (OISVM), which utilizes a small portion of data from the unseen placement or subject to online update the parameters of the SVM algorithm. The experimental results demonstrate the effectiveness of this OISVM algorithm on placement and subject variations.