Efficiency Of Support Vector Machine Research Articles

AtanK-A New SVM Kernel for Classification

Abstract The efficiency of support vector machine in practice is closely related to the optimal selection of kernel functions and their hyper-parameters. A novel kernel, namely the arctangent kernel, is proposed in this paper. Compared with the Gaussian kernel, the new proposed kernel has a quick similarity descent in the neighborhood of the inspection sample and a moderate similarity descent toward the infinity of the inspection sample. The experimental results on two simulated data sets and some UCI data sets show that the new proposed kernel function has better effectiveness and robustness compared with the polynomial kernel, the Gaussian kernel, the exponential radial basis function, and the former proposed kernel with moderate decreasing.

Performance Evaluation of Support Vector Machine Kernels in Intrusion Detection System for Wireless Sensor Network

Wireless sensor network is very popular in the industrial application due to its characteristics of infrastructure-less wireless network and self-configured for physical and environmental conditions monitoring. However, the dynamic environments of wireless network expose WSN to network vulnerabilities. Intrusion Detection System (IDS) has been used to mitigate the vulnerability issue of network. Researches towards the efficiency improvement of WSN-IDS has been extensively done because the rapid growth of technologies influence the growth of network attacks. Implementation Support Vector Machine (SVM) was found to be one of the optimum algorithms for the improvement of WSN-IDS. Yet, classification efficiency of SVM is based on the kernel function used because different kernel gives different SVM architecture. Linear classification of SVM has limitation to maximize the margin due to the dynamic environment of wireless network which consist of nonlinear data. Since maximizing the margin is the primary goal of SVM, it is crucial to implement the optimum kernel in the classification of nonlinear data. Each SVM model in this research use different kernels which are Linear, RBF, Polynomial and Sigmoid kernels. Further, NSL-KDD dataset was used for the experiment of this research. Performance of each kernel were evaluated based on the experimental result obtained and it was found that RBF kernel provides the best classification accuracy with the score of 91%. Finally, discussion based on the findings was made.

A Machine Learning Approach for Collusion Detection in Electricity Markets Based on Nash Equilibrium Theory

We aim to provide a tool for independent system operators to detect the collusion and identify the colluding firms by using day-ahead data. In this paper, an approach based on supervised machine learning is presented for collusion detection in electricity markets. The possible scenarios of the collusion among generation firms are firstly identified. Then, for each scenario and possible load demand, market equilibrium is computed. Market equilibrium points under different collusions and their peripheral points are used to train the collusion detection machine using supervised learning approaches such as classification and regression tree (CART) and support vector machine (SVM) algorithms. By applying the proposed approach to a four-firm and ten-generator test system, the accuracy of the proposed approach is evaluated and the efficiency of SVM and CART algorithms in collusion detection are compared with other supervised learning and statistical techniques.

Fog-Cloud centric IoT-based cyber physical framework for panic oriented disaster evacuation in smart cities

During disasters, individuals witness various dreadful scenes and situations. These scenes and situations generate a sense of panic and cause panic attacks to individuals. Panic attacks cause various disorders like Panic Disorder, Post Traumatic Stress Disorder, and Anxiety Disorder, etc. On the other side, smart cities are becoming the mainstay for urbanization. Hence, the increasing incidents of disruptions due to disasters require the smart cities to adopt emergency response and resilience as the most critical dimension for its design so that the disaster-related risks can be prevented and controlled. This dimension of smart city design helps in minimizing the disruption, human, and socio-economic loss through Information and Communication Technologies (ICT) and called smart disaster management. In this paper, a Fog-Cloud centric Internet of Things (IoT)-based cyber physical framework is proposed, which prioritizes the evacuation of the panicked stranded individuals and provides timely medical support. The physical subsystem of the framework acquires data from stranded individuals and disaster-affected environment and provides various information services to the respective stakeholders (evacuation personnel and stranded individuals). Whereas, the cyber subsystem of the proposed framework, initially at the Fog layer classifies the Panic Health Status (PHS) of the stranded individuals in real-time based on the acquired health data and analyzes the novelty of the data for avoiding unnecessary data traffic to Cloud. After PHS diagnosis, the cyber subsystem uses Bayesian Belief Network (BBN) to monitor the panic health sensitivity of the stranded panicked individuals using disaster-related health and environmental data, at the Cloud layer. This subsystem also builds the evacuation map using acquired disaster-related environmental data at the Cloud layer. Based on the evacuation map and monitored panic health sensitivity of the individuals, the subsystem prepares evacuation strategy, which prioritizes the evacuation of the stranded individuals. The vital points of this proposed framework are the immediate panic-related diagnostic and curative alert generation to the mobile devices of the stranded individuals from the Fog layer, and the preparation of the evacuation strategy based on the evacuation map and panic health sensitivity monitoring of the stranded individuals, at the Cloud layer. The experimental evaluation of the proposed framework depicts the classification efficiency of Support Vector Machine (SVM) for classifying the PHS of the stranded individuals in real-time, and efficiency of Data Novelty Analysis (DNA) for avoiding unnecessary data traffic, at the Fog layer. The experimental evaluation also acknowledges the efficiency of evacuation map building using Unmanned Aerial Vehicles (UAVs), and panic health sensitivity monitoring using BBN at the Cloud layer.

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Gully erosion is the most active hydro-geomorphological phenomenon in the continental areas due to the high erosion rates triggered by the gully system. Monitoring and modelling gully development and gully distribution will contribute to understand landforms evolution and risk assessment. The purpose of the current research is to model head-cut gully erosion susceptibility (HCGES) using support vector machine (SVM), random forest (RF) and novel ensemble model of random forest with four Oblique methods (Logistic Regression, Ridge Regression, Partial least squares (PLS) and Support vector machine (SVM)) (hereafter called ensemble ORF) data mining models in Fareghan watershed, Hormozghan province, Iran. For this purpose, 14 variables influencing the gully development, were prepared and 145 head-cut gully erosion locations were identified in the study area. The efficiency of SVM, RF, ensemble ORF were evaluated based on receiver operating characteristic (ROC), the results have shown that all these three models are highly accurate and robust in predicting the head-cut gully erosion susceptibility zones. The results of the models were evaluated based on the area under the receiver operatic characteristic curve (AUC) in the validation stage presented that the efficiency of these models are 0.91, 0.94, and 0.96, respectively. Altitude and distance from the road in all three models were more important than other variables. The findings of this research will contribute to develop gully control strategies and to prevent the gully initiation where gully erosion is more susceptible.

Prediction of Student\u2019s performance by modelling small dataset size

Prediction of student’s performance became an urgent desire in most of educational entities and institutes. That is essential in order to help at-risk students and assure their retention, providing the excellent learning resources and experience, and improving the university’s ranking and reputation. However, that might be difficult to be achieved for startup to mid-sized universities, especially those which are specialized in graduate and post graduate programs, and have small students’ records for analysis. So, the main aim of this project is to prove the possibility of training and modeling a small dataset size and the feasibility of creating a prediction model with credible accuracy rate. This research explores as well the possibility of identifying the key indicators in the small dataset, which will be utilized in creating the prediction model, using visualization and clustering algorithms. Best indicators were fed into multiple machine learning algorithms to evaluate them for the most accurate model. Among the selected algorithms, the results proved the ability of clustering algorithm in identifying key indicators in small datasets. The main outcomes of this study have proved the efficiency of support vector machine and learning discriminant analysis algorithms in training small dataset size and in producing an acceptable classification’s accuracy and reliability test rates.

Estimating Efficiency of Support Vector Machine Based Model in Prediction of The Direction of Future Stock Price During Different Trends of Market

Estimating Efficiency of Support Vector Machine Based Model in Prediction of The Direction of Future Stock Price During Different Trends of Market

A novel online incremental and decremental learning algorithm based on variable support vector machine

In view of the long execution time and low execution efficiency of Support Vector Machine in large-scale training samples, the paper has proposed the online incremental and decremental learning algorithm based on variable support vector machine (VSVM). In deep understanding of the operation mechanism and correlation algorithms for VSVM, each sample has increased training datasets changes and it needs to update the classifier of learning algorithm. Firstly, they are given the online growth amount of learning algorithm taken full advantage of the incremental pre-calculated information, and doesn’t require retraining for the new incremental training datasets. Secondly, the incremental matrix inverse calculation process had greatly reduced the running time of algorithm, and it is given in order to verify out the validity of the online learning algorithm. Finally, the nine groups of datasets in the standard library have been selected in the pattern classification experiment. The experimental results are shown that the online learning algorithm given in the case to ensure the correct classification rates and effective training’s speed. With the implementation of the incremental process, training meetings, the need for large-scale data storage space, result in slow training, the online learning algorithm based on VSVM can solve the problem.

Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record

The objective of this study was to consider the efficiency of support vector machine (SVM) and artificial neural network (ANN) for the classification and prediction of groundwater quality using a small data record in Malayer, Iran. For this purpose, 14 groundwater quality variables that had been collected from 27 groundwater sampling wells were used. Cluster analysis discriminated the total sampling stations into two groups. The classification was implemented by SVM using polynomial and RBF kernel methods. The respective sensitivity and specificity of this model were 0.89 and 0.80 while that of positive predictive value and negative predictive value were 0.89 and 0.86, respectively. The prediction of water quality index (WQI) was implemented using ANN. Despite the high correlation coefficient between the predicted and observed values of WQI(r = 0.90), the generalization ability of this model was low(r = 0.60) indicating the over-fitting of the model to the training data set.

Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity.

Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD). To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI) to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r) population. In this study, we examined the efficiency of support vector machine (SVM) classifier to successfully discriminate rMDD individuals from healthy controls (HCs) in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9%) by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention.

A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods.

An Advanced Multi Class Instance Selection based Support Vector Machine for Text Classification

Abstract Machine learning techniques is most commonly used technique in text mining. Support Vector Machine (SVM) is a most useful supervised learning technique for text classification. In this paper we proposed advanced Multi Class Instance Selection based support vector machine (AMCISSVM) to increasing efficiency of support vector machine. The proposed algorithm is compared with Multi Class Instance Selection (MCIS) and Neighborhood Property based Pattern Selection (NPPS) algorithm. The advanced MCIS has shown high accuracy to multi datasets. These experimental datasets are retrieved from UCI machine learning repositories.

Derivation of Monthly Operating Rules for Cascade Hydropower Stations Based on SVM

In order to promote the efficiency of actual hydropower system operation under limited inflow forecast, an Implicit Stochastic Optimization method using Support Vector Machine (SVM) theory is proposed in this paper to derive long-term optimal operating rules. By applying the model to the Jinsha-Yangtze river system which is the largest hydropower base in China, fitting performance of operating rules is explained and evaluated. System simulation results are given and compared to deterministic optimal operation. Power output processes comparison shows that the average annual system power generation in two scenarios are 395TWh and 392TWh, and the overall operation processes are in well accordance with explicable inconsistency, which proves the efficiency of SVM in operating rules derivation for hydropower stations.

Preprocessing unbalanced data using support vector machine

This paper deals with the application of support vector machine (SVM) to deal with the class imbalance problem. The objective of this paper is to examine the feasibility and efficiency of SVM as a preprocessor. Our study analyzes different classification algorithms that are employed to predict the customers with caravan car policy based on his/her sociodemographic data and history of product ownership. A series of experiments was conducted to test various computational intelligence techniques viz., Multilayer Perceptron (MLP), Logistic Regression (LR), and Random Forest (RF). Various standard balancing techniques such as under-sampling, over-sampling and Synthetic Minority Over-sampling TEchnique (SMOTE) are also employed. Subsequently, a strategy of data balancing for handling imbalanced distribution in data is proposed. The proposed approach first employs SVM as a preprocessor and the actual target values of training data are then replaced by the predictions of trained SVM. Later, this modified training data is used to train techniques such as MLP, LR, and RF. Based on the measure of sensitivity, it is observed that the proposed approach not only balances the data effectively but also provides more number of instances for minority class, which in turn enhances the performance of the intelligence techniques.

Combining partially global and local characteristics for improved classification

The Support Vector Machine (SVM) has achieved promising classification performance. However, since it is based only on local information (Support Vectors), it is sensitive to directions with large data spread. On the other hand, Nonparametric Discriminant Analysis (NDA) is an improvement over the more general Linear Discriminant Analysis (LDA) where, the normality assumption from LDA is relaxed. Furthermore, NDA incorporates the partially global information to detect the dominant normal directions to the decision surface, which represent the true data spread. However, NDA relies on the choice of the κ-nearest neighbors (κ-NN’s) on the decision boundary. This paper introduces a novel Combined SVM and NDA (CSVMNDA) model which controls the spread of the data, while maximizing a relative margin separating the data classes. This model is considered as an improvement to SVM by incorporating the data spread information represented by the dominant normal directions to the decision boundary. This can also be viewed as an extension to the NDA where the support vectors improve the choice of κ-nearest neighbors (κ-NN’s) on the decision boundary by incorporating local information. Since our model is an extension to both SVM and NDA, it can deal with heteroscedastic and non-normal data. It also avoids the small sample size problem. Interestingly, the proposed improvements only require a rigorous and simple combination of NDA and SVM objective functions, and preserve the computational efficiency of SVM. Through the optimization of the CSVMNDA objective function, surprising performance gains were achieved on real-world problems. In particular, the experiments on face recognition have clearly shown the superiority of CSVMNDA over other state-of-the-art classification methods, especially, SVM and NDA.

SVM-based active feedback in image retrieval using clustering and unlabeled data

In content-based image retrieval, relevance feedback is studied extensively to narrow the gap between low-level image feature and high-level semantic concept. However, most methods are challenged by small sample size problem since users are usually not so patient to label a large number of training instances in the relevance feedback round. In this paper, this problem is solved by two strategies: (1) designing a new active selection criterion to select images for user's feedback. It takes both the informative and the representative measures into consideration, thus the diversities between these images are increased while their informative powers are kept. With this new criterion, more information gain can be obtained from the feedback images; and (2) incorporating unlabeled images within the co-training framework. Unlabeled data partially alleviates the training data scarcity problem, thus improves the efficiency of support vector machine (SVM) active learning. Systematic experimental results verify the superiority of our method over existing active learning methods.

An adaptive error penalization method for training an efficient and generalized SVM

A novel training method has been proposed for increasing efficiency and generalization of support vector machine (SVM). The efficiency of SVM in classification is directly determined by the number of the support vectors used, which is often huge in the complicated classification problem in order to represent a highly convoluted separation hypersurface for better nonlinear classification. However, the separation hypersurface of SVM might be unnecessarily over-convoluted around extreme outliers, as these outliers can easily dominate the objective function of SVM. This situation eventually affects the efficiency and generalization of SVM in classifying unseen testing samples. To avoid this problem, we propose a novel objective function for SVM, i.e., an adaptive penalty term is designed to suppress the effects of extreme outliers, thus simplifying the separation hypersurface and increasing the classification efficiency. Since maximization of the margin distance of hypersurface is no longer dominated by those extreme outliers, our generated SVM tends to have a wider margin, i.e., better generalization ability. Importantly, as our designed objective function can be reformulated as a dual problem, similar to that of standard SVM, any existing SVM training algorithm can be borrowed for the training of our proposed SVM. The performances of our method have been extensively tested on the UCI machine learning repository, as well as a real clinical problem, i.e., tissue classification in prostate ultrasound images. Experimental results show that our method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.

Design efficient support vector machine for fast classification

This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM). First, a SVM is initially trained by all the training samples, thereby producing a number of support vectors. Second, the support vectors, which make the hypersurface highly convoluted, are excluded from the training set. Third, the SVM is re-trained only by the remaining samples in the training set. Finally, the complexity of the trained SVM is further reduced by approximating the separation hypersurface with a subset of the support vectors. Compared to the initially trained SVM by all samples, the efficiency of the finally-trained SVM is highly improved, without system degradation.