Support Vector Machine Parameter Optimization Research Articles

A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

Forecasting lake level at various horizons is a critical issue in navigation, water resource planning and catchment management. In this article, multistep ahead predictive models of predicting daily lake levels for three prediction horizons were created. The models were developed using a novel method based on support vector machine (SVM) coupled with firefly algorithm (FA). The FA was applied to estimate the optimal SVM parameters. Daily water-level data from Urmia Lake in northwestern Iran were used to train, test and validate the used technique. The prediction results of the SVM–FA models were compared to the genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results showed that an improvement in the predictive accuracy and capability of generalization can be achieved by the SVM–FA approach in comparison to the GP and ANN in 1 day ahead lake level forecast. Moreover, the findings indicated that the developed SVM–FA models can be used with confidence for further work on formulating a novel model of predictive strategy for lake level prediction.

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition (EMD) and support vector machine (SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions (IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm (GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

In most cases, fault diagnosis is essentially a pattern recognition problem and support vector machine (SVM) provides a new solution for the diagnosis problem of systems in which the fault samples are few. However, the parameters selection in SVM has significant influence on the diagnosis performance. In this paper, improved fruit fly optimization algorithm (IFOA), which is basically the standard fruit fly optimization algorithm (FOA) combined with Levy flight search strategy, is proposed to determine the SVM parameters. Some benchmark datasets are used to evaluate the proposed algorithm. Furthermore, the proposed method is used to diagnose the faults of hydraulic pump. Experiments and engineering application show that the proposed method outperforms standard FOA, genetic algorithm (GA) and particle swarm optimization (PSO) methods.

New Hybrid (SVMs -CSOA) Architecture for classifying Electrocardiograms Signals

a medical test that provides diagnostic relevant information of the heart activity is obtained by means of an ElectroCardioGram (ECG). Many heart diseases can be found by analyzing ECG because this method with moral performance is very helpful for shaping human heart status. Support Vector Machines (SVM) has been widely applied in classification. In this paper we present the SVM parameter optimization approach using novel metaheuristic for evolutionary optimization algorithms is Cat Swarm Optimization Algorithm (CSOA). The results obtained assess the feasibility of new hybrid (SVMs -CSOA) architecture and demonstrate an improvement in terms of accuracy.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

Hybrid Patterns Recognition of Control Chart Based on WA-PCAPSO-SVM

Based on the analysis of the defect of traditional model, this paper proposes a new control chart pattern recognition model, which includes Wavelet Analysis (WA), Principal Component Analysis (PCA), Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). WA is good to eliminate noise control chart anomaly pattern recognition of the adverse effect. PCA eliminates the redundant information of data between SVM and reduces the input dimension and computational complexity. PSO algorithm optimizes the parameters of SVM and the establishment of the optimal control chart anomaly pattern classifier can solve the problem optimal parameters of SVM. The simulation results show that the model is feasible, the results are reliable. This algorithm improves the control chart abnormal state average recognition accuracy and be used in the machining process real-time monitoring.

Transducer invariant multi-class fault classification in a rotor-bearing system using support vector machines

Faults in a rotor-bearing system due to bearings and unbalance have been classified using support vector machines (SVMs). Vibration signals on a rotor-bearing system were measured simultaneously at five different rotating speeds using seven transducers. The most sensitive feature of the vibration signals has been determined using compensation distance evaluation technique. Multi-class SVMs classification algorithm was then implemented for classification of the faults by considering SVMs created by the possible combinations of the most two sensitive features for each type of fault. By using optimal SVM parameters, the effective location of transducer among seven transducers for best classification of the faults has been investigated and found that any transducer provides a classification of 75% or better and this classification rate increases when more transducers are considered. This paper provides a robust SVM based technique using only time domain data without any additional preprocessing for classifying bearing and unbalance faults.

Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM

Due to the complex features of rock burst hazard assessment systems, a support vector machine (SVM) model for predicting of classification of rock burst was established based on the SVM theory and the actual characteristics of the project in this study. The main factors of rock burst, such as coal seam, dip, buried depth, structure situation, change of pitch angle, change of coal thickness, gas concentration, roof management, pressure relief and shooting were defined as the criterion indices for rock burst prediction in the proposed model. In order to determine reasonable and efficient the parameters of SVM, Firstly, the appropriate fitness function for genetic algorithms (GA) operation was determined, and then optimization parameters of SVM model were selected by real coded GA, therefore, the genetic algorithms and support vector machine (GSVM) model was established. A GSVM model was obtained through training 23 sets of measured data, the cross-validation method was introduced to verify the stability of GSVM model and the ratio of mis-discrimination is 0. Moreover, the proposed model was used to predict 12 new samples rock burst, the correct rate of prediction results is 91.6667% and are identical with actual situation. The results show that the genetic algorithm can speed up SVM parameter optimization search, the proposed model has a high credibility in the study of rock burst prediction of risk classification, which can be applied to practical engineering.

Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis

The performance of support vector machine (SVM) heavily depends on its parameters. The parameter optimization for SVM is still an ongoing research issue. The current parameter optimization methods either are easy to fall into local optimal solution, or are time consuming. Moreover, some optimization methods depend also on the choice of parameters for them, provoking thus a vicious circle. In view of this, a new hybrid method is proposed to optimize the parameters of SVM in this paper. It uses the inter-cluster distance in the feature space (ICDF) to determine a small and effective search interval from a larger kernel parameter search space, while a hybrid of the barebones particle swarm optimization and differential evolution (BBDE) is used to search the optimal parameter combination in the new search space. The ICDF shows the degree the classes are separated. The BBDE is a new, almost parameter-free optimization algorithm. Some benchmark datasets are used to evaluate the proposed algorithm. Furthermore, the proposed method is used to diagnose the faults of rolling element bearings. Experiments and engineering application show that the proposed method outperforms other methods both mentioned in this paper and published in other literature. A new hybrid method is used to optimize the parameters of SVM.ICDF measure is used to determine a small yet effective search interval for kernel parameter.An almost parameter-free evolutionary algorithm, BBDE, is used to search the optimal parameters of SVM.The proposed method is tested on several benchmark datasets as well as fault diagnosis for rolling element bearings.Experimental results show the outstanding performance of the proposed method.

A new sense-through-foliage target recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine

Sense-through-foliage target detection and recognition is of interest to both military and civilian research. In this paper, a new recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine (SVM) is proposed to recognize targets obscured by foliage. To seek the optimal parameters of SVM, a new hybrid differential evolution and self-adaptive particle swarm optimization (DEPSO) algorithm is developed to determine the optimal parameters for SVM with the highest accuracy and generalization ability. In this work, sparse representation is applied to extract the target features from real target echo waveforms measured by a bistatic ultra-wideband (UWB) radar system. Then, the extracted features are input into the proposed method to automatically recognize the types of targets. This method is validated by experiments taken in the forest environment. Compared with the commonly used particle swarm optimization-optimized SVM (PSO-SVM), SVM, k-nearest neighbor (KNN) and BP neural network (BPNN), the proposed DEPSO-SVM can achieve a higher accuracy. Experimental results demonstrate the effectiveness and robustness of the proposed method for sense-through-foliage target recognition.

Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City, China

AbstractAgricultural, environmental and ecological modeling requires soil cation exchange capacity (CEC) that is difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict CEC from easily measured physicochemical properties (e.g., texture, soil organic matter, pH). This study developed the support vector machines (SVM)‐based PTFs to predict soil CEC based on 208 soil samples collected from A and B horizons in Qingdao City, Shandong Province, China. The database was randomly split into calibration and validation datasets in proportions of 3:1 using the bootstrap method. The optimal SVM parameters were searched by applying the genetic algorithm (GA). The performance of SVM models was compared to those of multiple stepwise regression (MSR) and artificial neural network (ANN) models. Results show that the accuracy of CEC predicted by SVM improves considerably over those predicted by MSR and ANN. The performance of SVM for B horizon (R2 = 0.85) is slightly better than that for A horizon (R2 = 0.81). The SVM is a powerful approach in the simulation of nonlinear relationship between CEC and physicochemical properties of widely distributed samples from different soil horizons. Sensitivity analysis was also conducted to explore the influence of each input parameter on the CEC predictions by SVM. The clay content is the most sensitive parameter, followed by soil organic matter and pH, while sand content has the weakest influence. This suggests that clay is the most important predictor for predicting CEC of both soil horizons.

Parameter Optimization Using GA in SVM to Predict Damage Level of Non-Reshaped Berm Breakwater

In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA algorithm. The models are trained and tested on the data set obtained from the experiments which were carried out at Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. The results of SVM and GA-SVM models are compared in terms of statistical measures like correlation coefficient, root mean square error and scatter index. The results on SVM and GA-SVM models reveals that the performance of GA-SVM is better compared to SVM models in predicting the damage level of non-reshaped berm breakwater.

Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data

A multi-fault classification of gears has been attempted by support vector machine (SVM) learning techniques with the help of time–frequency (wavelet) vibration data. A suitable exploitation of SVM is based on the selection of SVM parameters. The main focus of the present paper is to study the performance of the multiclass capability of SVM techniques. Different optimization methods (i.e., the grid-search method (GSM), the genetic algorithm (GA) and the artificial bee colony algorithm (ABCA)) have been performed for optimizing SVM parameters. Four fault conditions of gears have been considered. The continuous wavelet transform (CWT) and wavelet packet transform (WPT) are estimated from time domain signals, and a set of statistical features are extracted from the wavelet transform. The prediction of fault classification has been attempted at the same angular speed as the measured data as well as innovatively at the intermediate and extrapolated angular speed conditions, since it is not feasible to have measurement of vibration data at continuous speeds of interest. The classification ability is noted and compared with predictions when purely time domain data is used, and it shows an excellent prediction performance.

A novel recognition system for human activity based on wavelet packet and support vector machine optimized by improved adaptive genetic algorithm

A new human activities recognition system based on support vector machine (SVM) optimized by improved adaptive genetic algorithm (IAGA) and wavelet packet is proposed. Wavelet packet transform (WPT) is applied to extract the signatures from various actions. SVM is a powerful tool for solving the classification problem with small sampling, nonlinearity and high dimension. Genetic algorithm (GA) is employed to determine the two optimal parameters for SVM with highest predictive accuracy and generalization ability. Moreover, the IAGA adopts the dynamic cross rate and mutation rate according to the group fitness, thus effectively avoiding the disadvantages of the standard GA, such as premature convergence and low robustness. The average recognition accuracy rate goes up to 97.6%. In addition, the result of suggested method is also compared with other feature extraction methods which further demonstrate the superiority of WPT and generalization ability of IAGA. The aforementioned results clearly demonstrate that the proposed method is superior to the traditional method in activity recognition.

A novel particle swarm optimization trained support vector machine for automatic sense-through-foliage target recognition system

Detection and recognition of targets that are embedded in foliage is of interest to both military and civilian research. Due to multipath propagation effects of rough surfaces, scattering from trees and ground tend to overwhelm the weak backscattering of targets, which makes it more difficult for sense through foliage target detection and recognition. In this paper, a novel intelligent recognition model based on support vector machine (SVM) and novel particle swarm optimization for sense through foliage targets recognition is proposed. SVM is a powerful novel tool for solving the recognition problem with small sampling, nonlinearity and high dimension. A new adaptive chaos particle swarm optimization (ACPSO) is developed in this study to determine the optimal parameters for SVM with the highest accuracy and generalization ability. Moreover, the measured real target echo signals are processed using sparse representation. Principal component analysis (PCA) is performed to extract the features of targets. Then, a hybrid feature selection is used to remove the redundant and irrelevant information of the features. The computational results on different real measurement datasets validate the effectiveness of the proposed approach.

Adaptive Power System Stabilizer Design Using Optimal Support Vector Machines Based on Harmony Search Algorithm

—This article presents the application of support vector machines to adaptive power system stabilizer design in a multi-machine power system based on the harmony search algorithm. Data from a multi-machine power system are the input features of the support vector machines. Support vector machine parameters and power system features are simultaneously optimized by harmony search based on the k-fold cross-validation technique. The proposed algorithm is trained by the optimal support vector machine parameters and optimal power system features. Power system stabilizer parameters produced by the proposed algorithm can be adapted by various operating conditions when the power system operates either inside or outside the training ranges. Simulation studies in the IEEJ Western Japan ten-machine power system demonstrate that the proposed algorithm is far superior to conventional power system stabilizers with fixed parameters and those designed by a robust coupled vibration model under various operating conditions and severe disturbances.

Network Intrusion Detection Based on PSO-SVM

In order to improve network intrusion detection precision, this paper proposed a network intrusion detection model based on simultaneous selecting features and parameters of support vector machine (SVM) by particle swarm optimization (PSO) algorithm. Firstly, the features and parameters of SVM are coded to particle, and then the PSO is used to find the optimal features and SVM parameters by collaboration among particles, lastly, the performance of the model was tested by KDD Cup 99 data. Compared with other network models, the proposed model has reduced input features for SVM and has significantly improved the detection precision of network intrusion. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3826

PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses.

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis

The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for SVM. An artificial chemical reaction optimization algorithm (ACROA) is a new method to solve the global optimization problem and is adapted to optimize SVM parameters. In this paper, a SVM parameter optimization method based on ACROA (ACROA-SVM) is proposed. Furthermore, the ACROA-SVM is applied to diagnose roller bearing faults. Firstly, the original modulation roller bearing vibration signals are decomposed into product functions (PFs) by using the local mean decomposition (LMD) method. Secondly, the ratios of amplitudes at the different fault characteristic frequencies in the envelope spectra of some PFs that include dominant fault information are defined as the characteristic amplitude ratios. Finally, the characteristic amplitude ratios are used as input to the ACROA-SVM classifiers, and the fault patterns of the roller bearing are identified. The result shows that the combination of this ACROA-SVM classifiers and LMD method can effectively improve the accurate rate of fault diagnosis and reduce cost time.

Network Traffic Prediction Model Based on Catfish-PSO-SVM

In order to improve the prediction accuracy of network traffic, this paper proposes a network traffic prediction model based on support vector machine (SVM) which parameters are optimized by catfish particle swarm optimization algorithm. Firstly, the parameters of SVM are encoded as a particle, and then catfish effect is introduced to overcome the defects of particle swarm optimization algorithm, the optimal parameters of SVM are obtained by the particle interactions, finally, the prediction model of network traffic is established according to the optimal parameters and the simulation experiments are carried out to test the performance of established model with network traffic data. The simulation results show that, compared with other prediction models of network traffic, the proposed model can quickly find the optimal parameters of SVM and has improved the prediction accuracy of network traffic.