Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Aiming at solving the problems of low accuracy and high false alarm rate for network intrusion detection, an algorithm of intrusion detection based on PSO and kernel Extreme learning is proposed in this article. Particle Swarm Optimization (PSO) is a kind of swarm intelligent algorithm. Kernel extreme learning machine (KELM) is a kind of classical learning method of kernel machine with its fast learning rate and strong generalization ability, but the selection of kernel function and parameters of KELM directly affects its classification performance. In this article, PSO is used to optimize the kernel parameters of kernel limit learning machine. The global kernel function with strong generalization ability of linear combination and the local kernel function with strong learning ability are used to form multi-kernel limit learning machine, which can improve the performance of single kernel Extreme Learning Machine (ELM) classifier. Finally, the performance of the algorithm is compared and analyzed through experiments and the experimental results verify the validity of this algorithm.

The parameters selection and optimization of kernel function is the core of support vector machine (SVM), which is closely related to the distribution of datasets. It can be obtained a series of different group index, either mapped by different kernel functions on the same dataset or did by same kernel function on the different subsets. We analyze the impact of the choice of kernel functions and parameters on the performance of SVM, and propose a GA-optimized weighted mixed kernel function of SVM based on information entropy (GA-IE-RBF-SVM). The algorithm uses the information entropy to improve the contribution of the features that are conducive to classification firstly to mitigate falling into a local optimum, then learn from the idea of multi-core learning to enhance the adaptability of the algorithm. The optimal genetic algorithm (GA) is used to select the type of mixed kernel function, kernel function parameters and error penalty factor. The experimental results show that compared with other similar algorithms, this algorithm has a higher classification accuracy rate and faster convergence speed.

Health monitoring and prognostics of axial piston pump is very helpful for the safety of aerial hydraulic system. Directing to the difficulties of measuring the wear loss between the valve plate and cylinder barrel, this paper presents a hybrid prediction method based on EMD (Empirical Mode Decomposition) and SVM (Support Vector Machine), in which EMD is used to get the pump's health state and PSO (Particle Swarm Optimization)-SVM is used to make a prediction for the pump's remaining useful lifetime. Oil-return flow was selected to indicate the wear condition of pump with several IMFs. SVM was trained by these IMFs, and then be used to predict the oil-return flow one-step ahead or multi-step ahead. In the SVM's training process, PSO was used to search the optimal parameter of SVM's kernel function. Applications show that hybrid prediction method has higher prediction precision and could be applied for the remaining useful lifetime prognostics.

In the kernel extreme learning machine, a single global kernel function or a local kernel function can not balance the contradiction between fitting and generalization well, so replace the single kernel function with a mixed kernel function to improve the learning and generalization ability of the model. Based on this, a Multi-Kernel Extreme Learning Machine (MKELM) algorithm was proposed. Considering the parameter sensitivity characteristics of the MKELM, the artificial fish swarm algorithm (AFSA) was used to optimize the parameters. In summary, a hybrid algorithm was proposed and applied to the field of transformer fault diagnosis. Compared with the extreme learning machine algorithm of single kernel function and SVM algorithm, the diagnostic accuracy of this algorithm is higher.

Kernel function is dominant in regression process of Support Vector Machine (SVM), it influences the prediction performance of SVM directly. Single local or global kernel function has limitation in generalization or learning capacity. The mixture kernel function with two good capacities had been structured, and it had been used in soft-sensor modeling of refining process. The contrast of simulation results show: the generalization ability of SVM based on mixture kernel function is better than the ones based on the single local or global kernel function, the algorithm had been applied in the refining process of HETAO wood industry factory, and the prediction result meets the requirement of craft production.

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.

Support vector machines were developed in recent years, which have large advantage over the traditional neural network on small sample set for classification. In all research fields of these learning machines, the selection of kernel function is the most important problem, which has a closed relationship with the performance of classification. But the research work in this field is not enough. In this paper we evaluate the performance of usual kernel functions for SVM theoretically, through observing and computing the kernel matrix. Base on this, we used the selected kernel functions to get a new mixed kernel function. Experiential data proved that the performance of SVM was improved by the mixed kernel function. If we select the weighted values properly, the correct rate even is 100%. This will not only gives us a method to get a new learning machine, but also give a reference for selecting kernel function.

Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter >100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter >0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma values in the kernel function had no effect on the accuracy of the LU/LC maps in either of the climate zones. These new findings on SVM image classification are directly applicable to LU/LC for planning and environmental and natural resource management.

Cancer is a threatening disease for the human race affecting most people around the world. The topmost reason for cancer demise across the globe is lung cancer and therefore there were many algorithms applied in predicting the survival rate of lung cancer patients. As a result of which the survival rate of lung cancer patients is increasing gradually. Support Vector Machine (SVM) technique have high accuracy than other technique in prediction. The performance of the SVM algorithm depends on the kernel function. In this paper, a comparison of three different kernel functions predicting the survival rate of a lung cancer patient with an efficient normalization technique is studied. Experiments are conducted in the dataset obtained from Cancer Imaging Archive (TCIA). Along with SVM kernel functions, five machine learning techniques were also used in predicting the survival rate of lung cancer. RBF_SVM with normalized data produced high accuracy of 97.72% compared to another algorithm. Various performance metrics such as accuracy, precision, recall, F1 score are used to evaluate the performance of the SVM kernel function.

This paper proposes a hybrid Electroencephalogram (EEG) classification approach based on grey wolf optimizer (GWO) enhanced support vector machines (SVMs) called GWO-SVM approach for automatic seizure detection. In order to decompose EEG into five sub-band components, the discrete wavelet transform (DWT) was utilized to extracted features set. Then, this features are used to train the SVM with radial basis function (RBF) kernel function. Further, GWO was used for selecting the significant feature subset and the optimal parameters of SVM in order to obtain a successful EEG classification. The experimental results proved that the proposed GWO-SVM approach, able to detect epileptic and could thus further enhance the diagnosis of epilepsy with accuracy 100%. Furthermore, the proposed approach has been compared with genetic algorithm (GA) with support vector machines (GA-SVMs) and SVM using RBF kernel function. The computational results reveal that GWO-SVM approach achieved better classification accuracy outperforms both GA-SVM and typical SVMs.

This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data. Doi: 10.28991/ESJ-2022-06-04-014 Full Text: PDF

The changes in China’s stock market are inseparable from the country’s economic development and macroeconomic regulation and control and have far-reaching significance in promoting China’s national economic growth. Compared with the Western developed capital market, China’s current stock market’s main smart investment strategy still has certain defects. Based on the SVM model, this paper establishes a predictive model that combines kernel parameters and parameter optimization to model. The mesh search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the parameters of the SVM under various kernel functions such as radial basis kernel function. The algorithm and particle swarm optimization algorithm optimize the parameters of the SVM to strengthen the applicability of the model in practice. The empirical results show that under the three-parameter optimization algorithms, the prediction results are higher than the random prediction accuracy, which indicates that it is effective to optimize the model by adjusting the parameters of the SVM. Among them, the SVM using the genetic algorithm parameter optimization under the radial basis kernel function shows the better prediction effect, which is the closest to the real value in the stock market forecast. The particle swarm algorithm supports the vector machine to predict the effect is slightly lower than the grid. Search method. In addition, through comparison experiments, the guess accuracy of BP neural network is worse than that of the support vector machine model before the adjustment. Finally, this paper uses the well-trained model to plan the stock smart investment plan.

Introduction Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein. Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage. The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics. Methods In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM). Results Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively. Discussion and conclusion Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

Search engines are an integral part of web information gathering and retrieval process, but they are significantly dependent upon the words or word phrases input by the end user. The search engine usually contributes no additional semantic data toward the information acquisition process. This paper presents an intelligent search agent "Web host access tool" (WHAT) based on support vector machines (SVM), which introduces the notion of queries conducted within a specific contextual meaning. Given a context and associated keywords that personalize the search history and preferences of the user, WHAT performs more intelligent resource filtering than conventional search engines, providing more relevant results while filtering the irrelevant references. Search results obtained in the form of text from different search engines are processed by the SVM based word classifier that arrange the results obtained according to user preference obtained from previous search processes. The text materials are processed by Latent Semantic Indexing (LSI) for creating a document matrix that gives the probability of a word occurrence in a specified context. For simplicity, this paper considers 5 different contexts: business, education, entertainment, news and information and tourism. The LSI coefficients were used by SVM to yield confidence levels for each search result and according to that the results were sorted and presented to the end user. As an alternative, least square support vector machines (LS-SVM) are also studied in this paper. The system is updated by the user that provides feedback regarding the search relevance, based on which the LSI coefficients are updated to ensure increased relevance in future searches. Three different types of kernel function were considered in this paper -Linear. Radial basis function (RBF) and polynomial kernel. Results claim that Linear SVM perform better than the others, not only in terms of classification accuracy but also in terms of training speed.