Algorithm Optimized Support Vector Machine Research Articles

A novel optimized SVM algorithm based on PSO with saturation and mixed time-delays for classification of oil pipeline leak detection

ABSTRACTIn this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of the traditional support vector machine (SVM) approaches with applications in analyzing data of oil pipeline leak detection. In the proposed saturated and mix-delayed particle swarm optimization (SMDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on which the velocity updating model switches from one to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, time-varying time-delays and distributed time-delays are introduced in the velocity updating model to respectively reflect the history of previous personal and global optimum particles. The introduction of saturation constraint ensures that the particles will convergence in case that the velocity of the particles is too large. Eight well-known benchmark functions are employed to evaluate the proposed SMDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the developed framework SMDPSO-based SVM algorithm is exploited in the problem of oil pipeline leak detection. Experiment results demonstrate that the SMDPSO-based SVM method is superior over other well-known classification algorithms.

Photovoltaic (PV) Power Prediction Based on ABC - SVM

Photovoltaic(PV) generation forecasting technology is an important part of the development of PV grid-connected system, which has a great impact on the reliability, stability and economy of the grid. Due to uncertain meteorological factors such as light intensity, temperature, humidity and wind speed, the output of PV system is random, intermittent and uncontrollable, which makes the prediction accuracy of PV power generation not high. In order to improve the accuracy of PV power generation forecasting, artificial bee colony (ABC) algorithm is used to optimize the support vector machine (SVM) model and predict the PV power generation: Firstly, the ABC algorithm is used to optimize the penalty factor C and the kernel function g of the SVM prediction model. Secondly, the optimized SVM algorithm is trained and tested. Finally, the ABC-SVM algorithm is used to predict the PV generation. The simulation results show that compared with the traditional SVM algorithm, the ABC-SVM algorithm has less control parameters, strong optimization ability, higher prediction accuracy and more stable system, which provides a certain scientific research value for PV power generation forecasting.

Assessing the authenticity of black pepper using diffuse reflectance mid-infrared Fourier transform spectroscopy coupled with chemometrics

Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) coupled with chemometrics was used to examine the possibility of verifying the origin and authenticity of black pepper in this study. DRIFTS were obtained for 150 pure black pepper samples of different geographical origin (Kerala, HaiNan, DongNai, Sarawak, Fujian) and 1200 fake black pepper (adulterated with sorghum or Sichuan pepper, doping levels from 5% to 50%, respectively) by recording within a spectroscopic range of 4000–400 cm−1. Collected data were treated using principal component analysis (PCA), genetic algorithm optimized support vector machine (GA-SVM) and partial least squares discriminant analysis (PLS-DA). PCA model of the DRIFTS revealed the clustering trend of the pure and fake black pepper samples. Accurate and reliable classification results were obtained by DRIFTS combined GA-SVM and PLS-DA model. For the pure black pepper sample, the training set and prediction set of GA-SVM and PLS-DA models all achieved 100% correct recognition rate. For the fake black pepper sample, the total recognition rate of GA-SVM and PLS-DA model were greater than 98% for the training sets and more than 96% for the predicted sets, respectively. The results of this study demonstrated that DRIFTS method combined with chemometric tools can be successfully applied to detect mislabeling and adulteration of black pepper.

Oil immersed transformer fault diagnosis based on cross entropy algorithm optimized support vector machine

With the increase of the voltage level of the power grid and the increase of its capacity, the probability of transformer failure is getting higher and higher. In order to discover the early latency faults of transformers, a cross-entropy algorithm was proposed to optimize the support vector machines. This method established a support vector machine classification model and used the cross-entropy algorithm to optimize the penalty factor and kernel function parameters. The transformer fault data is used to verify the classification model and compared with the test results of other algorithms. The results show that the accuracy of this algorithm for oil-immersed transformer fault diagnosis has reached 86.7%, which is higher than that of genetic algorithm and particle swarm algorithm, and iterating over 6 times of the fitness curve tends to be smooth and takes less time. After many tests, the forecast results are stable.

A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease

Abstract In healthcare sector, it is of crucial importance to accurately diagnose Alzheimer’s disease (AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration and provide early treatment for AD patients. In this paper, a framework is proposed for the diagnosis of AD, which consists of MRI images preprocessing, feature extraction, principal component analysis, and the support vector machine (SVM) model. In particular, a new switching delayed particle swarm optimization (SDPSO) algorithm is proposed to optimize the SVM parameters. The developed framework based on the SDPSO-SVM model is successfully applied to the classification of AD and MCI using MRI scans from ADNI dataset. Our developed algorithm can achieve excellent classification accuracies for 6 typical cases. Furthermore, experiment results demonstrate that the proposed algorithm outperforms several SVM models and also two other state-of-art methods with deep learning embedded, thereby serving as an effective AD diagnosis method.

Improvement of the regression model for spindle thermal elongation by a Boosting-based outliers detection approach

Data-driven regression methods are adept at modeling the spindle thermal elongation for error reduction purposes, but are limited in their generalization ability for the established model to take effect under different work conditions, where one prime cause is that the measurement errors and environmental disturbances would inevitably lead to successive outliers in the experimental training data, and the regression algorithms tend to over-fit to such outliers in the thermal datasets which are usually small. Here, an approach is proposed for the first time to detect and remove the suspected outliers in the spindle thermal data while preserve most of the informative data at the same time, so that the disturbances from the outliers can be minimized. The proposed approach for outliers detection is a combination of Boosting and SVR (BSOD), where the support vector machine for regression (SVR) is used as the weak learner in Boosting. Furthermore, the SVR, which is now widely considered as the most suitable regression tool for mapping the temperature-thermal elongation relationship for a spindle, is adopted to regression model the spindle thermal elongation, where the genetic algorithm (GA) is employed to determine the optimal parameter setting for the modeling. Effectiveness of the BSOD approach was verified using four experimental datasets from two different spindles of precision boring machines under different work conditions. The results showed that the BSOD approach reduced the mean squared error MSE of the genetic algorithm optimized support vector machine estimation for unseen thermal elongation datasets in two cases by 66.09% and 49.13%, respectively. This research provides a feasible way to enhance the modeling quality for the thermal elongation of spindles during working process.

Color difference classification based on optimization support vector machine of improved grey wolf algorithm

In order to establish the color difference classification model of printing and dyeing products, a grey wolf algorithm optimization support vector machine based on differential evolution (DE) model is proposed in this paper. First of all, the performance of the support vector machine (SVM)model is mainly affected by the penalty parameter C and the RBF kernel width γ, and the method uses the good global search capability of grey wolf optimization (GWO) algorithm iteratively optimization to compute the best parameter combination of support vector machines. At the same time, because the initial population of grey wolf algorithm has a greater influence on the solution speed and quality of the algorithm, the DE algorithm is used to generate a more suitable initial population for grey wolf algorithm, which makes the grey wolf population have better solution ability. Finally, through the optimization to the penalty factor and the kernel width parameter, the printing and dyeing products classification model of SVM with strong generalization ability is constructed. The experimental results show that the proposed method achieves high classification accuracy, and have good stability and generalization ability, when it is compared with the color difference classification method of printing and dyeing product based on SVM and GWO-SVM algorithm.

Photovoltaic array prediction on short-term output power method in Centralized power generation system

The photovoltaic array directly determines the output power system of the entire photovoltaic power generation system. In order to more accurately predict the output power of the photovoltaic power generation system and reduce the impact of photovoltaic power generation on the power system, this study proposes a prediction model based on an improved firefly algorithm optimized support vector machine. The model introduces the linear decreasing inertia weight and the adaptive variable step-size in the original firefly algorithm that effectively improves the convergence speed and optimization ability of the algorithm. The multiple meteorological factors influencing the photovoltaic power generation were studied. Calculate the correlation coefficient of each meteorological influencing factor between the forecasted date and historical date to determine the training sample. The training samples were used to train the prediction model. The photovoltaic array output power in the sunny, cloudy and rainy days was predicted for the three weather styles using the trained prediction model. The results were compared the prediction results on the standard firefly algorithm-based optimizing support vector machine and particle swarm algorithm-based optimizing support vector machines. The proposed method showed that the mean absolute percentage error of the three-weather style prediction result is reduced by 1.66 and 3.30% and the mean square error is reduced by 0.21 and 0.27 compared to other methods. This method is verified to predict the PV array output power more accurately.

Comparison of three officinal species of Callicarpa based on a biochemome profiling strategy with UHPLC-IT-MS and chemometrics analysis

Traditional Chinese medicine (TCM) materials with closely related species are frequently fungible in clinical use. Therefore, holistic comparison of the composition in bioactive compounds is essential to evaluate whether they are equivalent in efficacy. Taking three officinal species of Callicarpa as a case, we proposed and validated a standardized strategy for the discrimination of closely related TCM materials, which focused on the extraction, profiling and multivariate statistical analysis of their biochemome. Firstly, serial liquid-liquid extractions were utilized to prepare different batches of Callicarpa biochemome, and the preparation yields were utilized for the normalization of sampling quantity prior to UHPLC-IT-MS analysis. Secondly, 34 compounds, including 19 phenylethanoid glycosides, 10 flavonoids and 5 terpenoids, were identified based on an untargeted UHPLC-IT-MS method. Thirdly, method validation of linearity, precision and stability showed that the UHPLC-IT-MS system was qualified (R2>0.995, RSD<15%) for subsequent biochemome profiling. After PCA and PLS-DA analysis, 30 marker compounds were screened and demonstrated to be of good predictability using genetic algorithm optimized support vector machines. Finally, a heatmap visualization was employed for clarifying the distribution of marker compounds, which could be helpful to determine whether the three Callicarpa species are, in fact, equivalent substitutes. This study provides a standardized biochemome profiling strategy for systemic comparison analysis of closely related TCM materials, which shows promising perspectives in tracking the supply chain of pharmaceutical suppliers.

The Application of Extreme Learning Machine and Support Vector Machine in Speech Endpoint Detection

In this paper, a general voice activity detection (VAD) method based on pattern recognition is proposed, and a specific algorithm of endpoint detection is researched. In this method, the Extreme Learning Machine (ELM) and Genetic Algorithm (GA) optimization Support Vector Machine (SVM) is used as the training and recognition model. The simulation results indicates that ELM and GA-SVM have the same superior endpoint detection accuracy, and recognition time were similar, but the training time of ELM only up to a 1/2000 of the GA-SVM, the robustness of ELM and GA-SVM is greatly improved in noisy environment compare with the traditional VAD that depends on time-domain energy and zero crossing rate.

Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine

The squeak and rattle (S&R) noise of a vehicle’s suspension shock absorber substantially influences the psychological and physiological perception of passengers. In this paper, a state-of-the-art method, specifically, a genetic algorithm-optimized support vector machine (GA-SVM), which can select the most effective feature subsets and optimize the model’s free parameters, is proposed to identify this specific noise. A vehicular road test and a shock absorber rig test are conducted to investigate the relationship between these features, and then an approach for quantifying the shock absorber S&R noise is given. Pre-processed signals are decomposed through a wavelet packet transform (WPT), and two criteria, namely, the wavelet packet energy (WPE) and wavelet packet sample entropy (WPSE), are introduced as the feature extraction methods. Then, the two extracted feature sets are compared based on this genetic algorithm. Another advanced method, known as the genetic algorithm-optimized back propagation neural network (GA-BPNN), is introduced for comparison to illustrate the superiority of the newly developed GA-SVM model. The result shows that the WPSE can extract more useful features than the WPE and that the GA-SVM is more effective and efficient than the GA-BPNN. The proposed approach could be retrained and extended to address other fault identification problems.

Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM

Aiming at the problems of low accuracy, poor universality and functional singleness for seizure detection, an effective approach using wavelet-based non-linear analysis and genetic algorithm optimized support vector machine (GA-SVM) is proposed to deal with five challenging classification problems in this study. Instead of the traditional discrete wavelet transform (DWT), we attempt to explore the ability of double-density discrete wavelet transform (DD-DWT) to decompose the original EEG into specific sub-bands. The Hurst exponent (HE) and fuzzy entropy (FuzzyEn) are extracted as input features and then fed into two classifiers. On using these ranking non-linear features, the GA-SVM configured with fewer features is found to achieve the prominent classification performance for various combinations such as AB-CD-E, A-D-E, ABCD-E, C-E and D-E, achieving accuracies of 99.36%, 99.60%, 99.40%, 100% and 100%, respectively. The results have indicated that our scheme is not only appropriate in solving problems with multiple classes but also of lower complexity and better expansibility. These characteristics would make this method become an attractive alternative for actual clinical diagnosis.

Structural characterization and discrimination of Chinese medicinal materials with multiple botanical origins based on metabolite profiling and chemometrics analysis: Clematidis Radix et Rhizoma as a case study

Traditional Chinese medicines (TCMs)-based products are becoming more and more popular over the world. To ensure the safety and efficacy, authentication of Chinese medicinal materials has been an important issue, especially for that with multiple botanical origins (one-to-multiple). Taking Clematidis Radix et Rhizoma (CRR) as a case study, we herein developed an integrated platform based on metabolite profiling and chemometrics analysis to characterize, classify, and predict the “one-to-multiple” herbs. Firstly, the predominant constituents, triterpenoid saponins, in three Clematis CRR were rapid characterized by a novel UPLC-QTOF/MS-based strategy, and a total of 49 triterpenoid saponins were identified. Secondly, metabolite profiling was performed by UPLC-QTOF/MS, and 4623 variables were extracted and aligned as dataset. Thirdly, by using pattern recognition analysis, a clear separation of the three Clematis CRR was achieved as well as a total number of 28 variables were screened as the valuable variables for discrimination. By matching with identified saponins, these 28 variables were corresponding to 10 saponins which were identified as marker compounds. Fourthly, based on the relative intensity of the marker compounds-related variables, genetic algorithm optimized support vector machines (GA-SVM) was employed to predict the species of CRR samples. The obtained model showed excellent prediction performance with a prediction accuracy of 100%. Finally, a heatmap visualization was employed for clarifying the distribution of identified saponins, which could be useful for phytochemotaxonomy study of Clematis herbs. These results indicated that our proposed platform was a powerful tool for chemical profiling and discrimination of herbs with multiple botanical origins, providing promising perspectives in tracking the formulation processes of TCMs products.

Coke Rate Prediction Model Based on Genetic Algorithm Optimized Support Vector Machine

The accurate prediction model’s establishing of the blast furnace coke rate is important for optimizing the integrated production indicators of iron and steel enterprise. For the problem of accuracy of the model of coke rate, This paper established blast coke rate modeling with support vector machine algorithm, the model parameters of support vector machine was optimized by genetic algorithm, then a coke rate model based on support vector machine with the best parameters was built. Simulation results showed that: the forecasting model’s outcome, average absolute error and the mean relative error, was small which is based on genetic algorithm optimized SVM. coke rate model based on Genetic algorithm optimized support vector machine has high degree of accuracy and a certain practicality.

Characterization and classification of seven Citrus herbs by liquid chromatography–quadrupole time-of-flight mass spectrometry and genetic algorithm optimized support vector machines

Citrus herbs have been widely used in traditional medicine and cuisine in China and other countries since the ancient time. However, the authentication and quality control of Citrus herbs has always been a challenging task due to their similar morphological characteristics and the diversity of the multi-components existed in the complicated matrix. In the present investigation, we developed a novel strategy to characterize and classify seven Citrus herbs based on chromatographic analysis and chemometric methods. Firstly, the chemical constituents in seven Citrus herbs were globally characterized by liquid chromatography combined with quadrupole time-of-flight mass spectrometry (LC–QTOF-MS). Based on their retention time, UV spectra and MS fragmentation behavior, a total of 75 compounds were identified or tentatively characterized in these herbal medicines. Secondly, a segmental monitoring method based on LC-variable wavelength detection was developed for simultaneous quantification of ten marker compounds in these Citrus herbs. Thirdly, based on the contents of the ten analytes, genetic algorithm optimized support vector machines (GA-SVM) was employed to differentiate and classify the 64 samples covering these seven herbs. The obtained classifier showed good prediction performance and the overall prediction accuracy reached 96.88%. The proposed strategy is expected to provide new insight for authentication and quality control of traditional herbs.

Performance assessment of different data mining methods in statistical downscaling of daily precipitation

Summary In this paper, nonlinear Data-Mining (DM) methods have been used to extend the most cited statistical downscaling model, SDSM, for downscaling of daily precipitation. The proposed model is Nonlinear Data-Mining Downscaling Model (NDMDM). The four nonlinear and semi-nonlinear DM methods which are included in NDMDM model are cubic-order Multivariate Adaptive Regression Splines (MARS), Model Tree (MT), k -Nearest Neighbor ( k NN) and Genetic Algorithm-optimized Support Vector Machine (GA-SVM). The daily records of 12 rain gauge stations scattered in basins with various climates in Iran are used to compare the performance of NDMDM model with statistical downscaling method. Comparison between statistical downscaling and NDMDM results in the selected stations indicates that combination of MT and MARS methods can provide daily rain estimations with less mean absolute error and closer monthly standard deviation and skewness values to the historical records for both calibration and validation periods. The results of the future projections of precipitation in the selected rain gauge stations using A2 and B2 SRES scenarios show significant uncertainty of the NDMDM and statistical downscaling models.

A New Method for Fault Diagnosis of Mine Hoist based on Manifold Learning and Genetic Algorithm Optimized Support Vector Machine

This paper reports a new development based on the manifold learning and intelligent classifier for the nonlinear feature extraction and fault pattern recognition of mine hoists. The wavelet packet was firstly used to extract the statistic characteristics of the hoist vibrations to obtain the original feature space. Then the locally linear embedding (LLE) was employed to learn the underlying nonlinear manifold in the original feature space to select distinct features. Following, the support vector machine (SVM) was applied to the fault pattern recognition. The energy-entropy based genetic algorithm was used to optimize the SVM parameters. The experimental vibration data measured on a mine hoist test rig was used to evaluate the proposed method. The diagnosis results show that the proposed method is efficient for the mine hoist and can increase the detection rate by 2.5% or better when compared with existing diagnosis approaches. Ill. 8, bibl. 10, tabl. 1 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.123.7.2385

Study on Genetic Algorithm Optimization for Support Vector Machine in Network Intrusion Detection

Abstract This paper studies on methods taken in solving the existing network disorders in network intrusion detection. Traditional parameter-optimized Support Vector Machine (SVM) may easily generate improper parameter-selections, and may further lead to a low accuracy in network intrusion detection. In order to overcome such problems, so well as to ensure the network security, this paper tries to put forward with a Genetic Algorithm optimized Support Vector Machine in network intrusion detection. For this purpose, some procedures have to be taken. Firstly the network intrusion data should be normalized and simplified for inputs, and then to obtain optimal parameters through the parameteroptimization for SVM with a Genetic Algorithm; Finally to get network intrusion results through detecting the network data normalized by an optimal Support Vector Machine (SVM) model. The simulating results show that, compared with the traditional network intrusion detection methods, the optimized Support Vector Machine (SVM) model with a Genetic Algorithm has a high accuracy, and a low rate of losing or false alarms, and is proved to be effective in network intrusion detection.

Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines

The chemical modification of histones at specific DNA regulatory elements is linked to the activation, inactivation and poising of genes. A number of tools exist to predict enhancers from chromatin modification maps, but their practical application is limited because they either (i) consider a smaller number of marks than those necessary to define the various enhancer classes or (ii) work with an excessive number of marks, which is experimentally unviable. We have developed a method for chromatin state detection using support vector machines in combination with genetic algorithm optimization, called ChromaGenSVM. ChromaGenSVM selects optimum combinations of specific histone epigenetic marks to predict enhancers. In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells. Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4+ T cells to predict ∼21 000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%. The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks.

An Internet Traffic Identification Approach Based on GA and PSO-SVM

Internet traffic identification is currently an important challenge for network management. Many approaches have been proposed to classify different categories of Internet traffic. However, traditional approaches only focus on identifying TCP flows and have ignored the selection of best feature subset for classification. In this paper, we propose an approach to classify both TCP and UDP traffic flows using the Support Vector Machine (SVM) algorithm. In this approach, we select the best feature subset using Genetic Algorithm, and then we calculate the correspondence weight of each feature selected by Particle Swarm Optimization (PSO). In addition, the traditional SVM algorithm is optimized by PSO algorithm. The experimental results demonstrate that this approach can effectively select the feature subset from multiple attributes that can best reflect the differences among different network applications. Moreover, the identification rate is improved by the method of feature weighting and PSO optimized SVM algorithm.