Support Vector Machine Parameter Optimization Research Articles

A novel SVM parameter tuning method based on advanced whale optimization algorithm

The classification performance of support vector machine (SVM) algorithm is highly dependent on the careful tuning of hyper-parameters and penalty coefficient. This paper introduces a novel SVM parameter optimization method by using the advanced whale optimization algorithm (AWOA) that is an improved whale of algorithm (WOA) with external archiving strategy. A new framework for SVM parameter optimization based on AWOA is built. To demonstrate the performance of our proposed method, six typical data sets are chosen to evaluate the effect of SVM classification problem. Experimental results show that the higher accuracy and better convergence can be achieved by AWOA compared with other three usual parameter optimization methods (WOA, PSO, and DE).

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weighting” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simultaneously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root-mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process.

Optimizing SVM’s parameters based on backtracking search optimization algorithm for gear fault diagnosis

The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for the SVM. The Backtracking Search Optimization Algorithm (BSA) is often applied to resolve the global optimization problem and adapted to optimize SVM parameters. In this research, a SVM parameter optimization method based on BSA (BSA-SVM) is proposed, and the BSA-SVM is applied to diagnose gear faults. Firstly, a gear vibration signal can be decomposed into several intrinsic scale components (ISCs) by means of the Local Characteristics-Scale Decomposition (LCD). Secondly, the MPE can extract the fault feature vectors from the first few ISCs. Thirdly, the fault feature vectors are taken as the input vectors of the BSA-SVM classifier. The analysis results of BSA-SVM classifier show that this method has higher accuracy than GA (Genetic Algorithm) or PSO (Particles Swarm Algorithm) algorithms combined with SVM. In short, the BSA-SVM based on the MPE-LCD is suitable to diagnose the state of health gear.

Optimizing Support Vector Machine Parameters based on Quantum and Immune Algorithm

Optimizing Support Vector Machine Parameters based on Quantum and Immune Algorithm

A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA‐SVM Based on Vibration Signal Analysis

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time‐spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog‐leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA‐SVM), and SFLA‐SVM. The experiment results show that MSFLA‐SVM achieves a much higher fault classification rate than BPNN, ACROA‐SVM, and SFLA‐SVM.

A Method for Guaranteeing Wireless Communication Based on a Combination of Deep and Shallow Learning

Wireless communication has changed and improved people's lives and society, especially with the arrival of the Internet of Things (IoT) era. Despite the maturity of wireless communication, the security issue of communication remains the most stubborn and troublesome problem due to the increasingly complex and large amounts of data. An intrusion detection system is the guarantee of secure communication. However, variable protocols and drastic growth in data volume make intrusion detection a difficult task. In this paper, we proposed a framework of anomaly-based network intrusion detection system to finish the detection job. First, UNSW-NB15 is selected as the research object. Based on this new dataset, we built a detection model combining a deep learning method and a shallow learning approach. The former one is a deep auto-encoder used for feature learning, which can discover important representations of data and accelerate detection. The latter one is a powerful support vector machine (SVM), where the artificial bee colony (ABC) algorithm is used to find optimal parameters for SVM with five-fold cross validation (5FCV). Various experiments are conducted and the simulation results prove that the proposed method performs quite better than some of state-of-the-art intrusion detection approaches, including the method based on the principal component analysis (PCA) and some other machine learning strategies.

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Under the background of “innovation and entrepreneurship,” how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF‐CSCA‐SVM, for predicting college students′ entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF‐CSCA‐SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students’ decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta‐heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

Correlation between 13C NMR chemical shifts and complete sets of descriptors of natural coumarin derivatives

Choosing the best set of descriptors for quantitative structure-property relationship (QSPR) is subjective and different descriptor set may be obtained for the same case. A complete set of descriptors means that the set is perfect since there is no any element redundant or need to be added. Here we report the first application of complete sets of descriptors calculated with PBE1PBE/6-311G(2d,2p) and B3LYP/6-31G(d) approaches to develop QSPR models for 13C NMR chemical shifts (δC parameters) of carbon atoms in coumarin derivatives. Four QSPR models for δC parameters were developed with support vector machine (SVM) algorithm, by applying the particle swarm optimization (PSO) technique to optimize SVM parameters C and γ. The four SVM models based on complete sets of descriptors have root mean square (rms) errors of 1.962 ppm, 2.145 ppm, 1.975 ppm and 2.363 ppm for the total data set (315 δC parameters), which are less than the rms errors from multiple linear regression (MLR) models. To check the method based on complete sets, a large diverse dataset was predicted. Moreover, the total data set from 35 coumarin derivatives was predicted by using ChemDraw to make a head-to-head comparison. Results of the study suggest that applying complete sets of descriptors for QSPR models is successful.

Complete sets of descriptors for the prediction of 13C NMR chemical shifts of quinoline derivatives

AbstractNowadays, there is no criterion to judge whether a descriptor subset has redundant structure information or not, although some criteria can be used to validate the quality of quantitative structure‐activity relationship (QSAR) models. This paper reports complete sets of descriptors used for selecting the subset of descriptors for QSAR. Four complete sets of descriptors calculated with B3LYP/6‐31G(d) and PBE1PBE/6‐311G(2d,2p) approaches were used to develop four QSAR models for 269 13C nuclear magnetic resonance (NMR) chemical shifts (δC parameters) of carbon atoms in 26 quinoline derivatives. Four QSAR models for δC, parameters were constructed with support vector machine (SVM) algorithm by applying genetic algorithm (GA) to optimize SVM parameters C and γ. The four SVM models have root‐mean‐square (RMS) error range of 2.0 ppm to 2.7 ppm. Compared with previous QSAR models for 13C NMR chemical shifts, the prediction results are accurate, which suggest that applying complete sets of descriptors for QSAR models is successful.

Assessment of Land Cover Changes Using Taguchi-Based Optimized SVM Classification Approach

Land use and land cover mapping is of great importance in many research areas. The main objective of this study was to assess land cover changes using support vector machine (SVM) classification approach. The most important challenge in this case is to determine the optimum structure of classification method. The optimization Taguchi method was performed to optimize the structure of SVM. Results showed that Taguchi method can be effectively used to cope with this problem. In fact, using Taguchi method to optimize SVM parameters can significantly decrease the number of classification tests. In this study, land use/cover maps of the Yazd–Ardakan plain were produced in three dates of 1990, 2002 and 2015. Maximum and minimum accuracy of the classification was 0.9 and 0.85 for 2015 and 1990 imagery, respectively. This may be related to the differences in the quality of the imagery. The land use/cover changes were then assessed based on the best classification results. It was shown that maximum change in the land use/cover was taken place in residential areas.

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.

Network Security Situation Assessment Model Based on GSA-SVM

In order to solve the problem of insufficient accuracy caused by the selection of support vector machine (SVM) parameters in situation assessment, a network security situation assessment model (GSA-SVM) based on GSA optimization of SVM parameters is proposed, which combines the characteristics of less parameters needed to be set by gravity search algorithm (GSA) and strong global optimization capability. Firstly, the model receives the situation assessment data evaluated by the expert system, and then searches for the optimal parameters in SVM through GSA, minimize the error between the generated data and the actual network security situation assessment data. The effect of the model is verified by using Eggcrate function and actual situation assessment data. The results show that this method has good learning ability and is better than Particle Swarm Optimization (PSO) in SVM optimization.

Expansion prediction of alkali aggregate reactivity-affected concrete structures using a hybrid soft computing method

The phenomenon of alkali aggregate reactivity (AAR) in concrete structures corresponds to the reaction between aggregates with some ingredients and alkali hydroxide in concretes. This AAR could potentially lead to concrete deformation, micro-cracks and eventually wide visible cracks. In this study, to predict the expansion of the concrete caused by AAR, a novel hybrid model is proposed based on support vector machine (SVM). In the proposed model, the inputs are the aggregate components and concrete age, while the output is the induced expansion in the concrete. To improve the generalisation capacity of the proposed model, the enhanced particle swarm optimisation algorithm is employed to select optimal SVM parameters. The proposed method is evaluated and compared with other conventional soft computing methods based on the experimental data. Finally, the evaluated results endorse the effectiveness of the proposed hybrid model.

Predict Coordinated Development Degree of County Eco-Environment System Using GA-SVM

This article describes how economic development has had a significant impact on the environment. County eco-environment coordinated development has contributed to regional coordinated development in China. A support vector machine (SVM) model was constructed to classify and predict coordinated development degrees of the county eco-environment system. In order to improve the discrimination precision of SVM in classification, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding coordinated development degree of county eco-environment system prediction for Guanzhong urban agglomeration. It found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient. The simulation indicates that the county slowing-down of economic development would not have positive effect on the environment sustainability. GA-SVM provides an effective measurement for region eco-environment system classification and prediction.

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)

Abstract In this study, wavelet-support vector machine (WSVM) is proposed for drought forecasting using the Standardized Precipitation Index (SPI). In this way, the SPI time series of Urmia Lake watershed is decomposed to multiple frequency time series by wavelet transform. Then, these time sub-series are applied as input data to the support vector machine (SVM) model to forecast drought. Also, a cuckoo search (CS)-based approach is proposed for parameter optimization of SVM, finding the best initial constant parameters of the SVM algorithm. The obtained results indicate that the radial basis function (RBF)-kernel function of the SVM algorithm has high efficiency in the SPI modeling, resulting in a determination coefficient (DC) of 0.865 in verification step. In the WSVM model, the Coif1, which is considered as a mother wavelet function with decomposition level of five, shows a better performance with DC of 0.954 in verification step, revealing that the proposed hybrid WSVM model outperforms the single SVM model in forecasting SPI time series. Also, DC of cuckoo search-support vector machine (CS-SVM) is calculated to be 0.912 in verification step, indicating the fact that the proposed CS-SVM model shows better efficiency than single SVM model.

Image Interpolation Scheme based on SVM and Improved PSO

In order to obtain visually pleasing images, a support vector machines (SVM) based interpolation scheme is proposed, in which the improved particle swarm optimization is applied to support vector machine parameters optimization. Training samples are constructed by the pixels around the pixel to be interpolated. Then the support vector machine with optimal parameters is trained using training samples. After the training, we can get the interpolation model, which can be employed to estimate the unknown pixel. Experimental result show that the interpolated images get improvement PNSR compared with traditional interpolation methods, which is agrees with the subjective quality.

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.

New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic

In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one.

A Brief Overview on Parameter Optimization of Support Vector Machine

Support vector machine (SVM) has been successfully applied in classification and regression problems. But it is very sensitive to the selection of parameters. The fundamental principles of SVM are analyzed firstly. The main optimization methods and achievements for SVM parameters are introduced. And the popular fitness functions used for the parameter optimization of SVM are described. The objective of this paper is to provide readers a brief overview of the recent advances for parameter optimization of SVM and enable them to develop and implement new optimization strategies for SVM-related research at their disposal.