Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Feature Extraction of Stored-grain Insects Based on Ant Colony Optimization and Support Vector Machine Algorithm

[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.Support Vector Machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

Parameter selection of support vector machine for function approximation based on chaos optimization

The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.

Identifcation of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine

An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.

Support vector machine forecasting method improved by chaotic particle swarm optimization and its application

By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.

Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

A new support vector machine optimized by improved particle swarm optimization and its application

A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

Design of Ballistic Consistency Based on Least Squares Support Vector Machine and Particle Swarm Optimization

In order to improve the firing efficiency of projectiles,it is required to use the universal firing table for gun weapon system equipped with a variety of projectiles.Moreover,the foundation of sharing the universal firing table is the ballistic matching for two types of projectiles.Therefore,a method is proposed in the process of designing new type of projectile.The least squares support vector machine is utilized to build the ballistic trajectory model of the original projectile,thus it is viable to compare the two trajectories.Then the particle swarm optimization is applied to find the combination of trajectory parameters which meet the criterion of ballistic matching best.Finally,examples show the proposed method is valid and feasible.

Analysis of Water Pollution Causes and Control Countermeasures in Liaohe Estuary via Support Vector Machine Particle Swarm Optimization under Deep Learning

This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration ofwater quality.Aprediction systembased on support vectormachine(SVM)-particle swarm optimization(PSO)(SVM-PSO)algorithm is proposed under the background of deep learning.SVM-PSO algorithm is employed to analyze the pollution status of the Liaohe estuary,so is the difference in water pollution of different sea consuming types.Based on the analysis results for causes of pollution,the control countermeasures of water pollution in Liaohe estuary are put forward.The results suggest that the water pollution index prediction model based on SVM-PSO algorithm shows the maximum error of 2.41%,the average error of 1.24%in predicting the samples,the root mean square error(RMSE)of 5.36×10^(−4),and the square of correlation coefficient of 0.91.Therefore,the prediction system in this study is feasible.At present,the water pollution status of Liaohe estuary is of moderate and severe levels of eutrophication,and the water pollution status basically remains at the level of mild pollution.The general trend is fromphosphorus moderate restricted eutrophication to phosphorus restricted potential eutrophication.To sumup,the SVM-PSO algorithm shows good sewage prediction ability,which can be applied and promoted in water pollution control and has reliable reference significance.

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.The usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models.The PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS SVM.The performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and SVM.For PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM models.The PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.

Hybrid Optimization of Support Vector Machine for Intrusion Detection

Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.

Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines

Support vector machine (SVM) is a popular pattern classification method with many application areas. SVM shows its outstanding performance in high-dimensional data classification. In the process of classification, SVM kernel parameter setting during the SVM training procedure, along with the feature selection significantly influences the classification accuracy. This paper proposes two novel intelligent optimization methods, which simultaneously determines the parameter values while discovering a subset of features to increase SVM classification accuracy. The study focuses on two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine above the two intelligent optimization methods with SVM to choose appropriate subset features and SVM parameters, which are termed GA-FSSVM (Genetic Algorithm-Feature Selection Support Vector Machines) and PSO-FSSVM(Particle Swarm Optimization-Feature Selection Support Vector Machines) models. Experimental results demonstrate that the classification accuracy by our proposed methods outperforms traditional grid search approach and many other approaches. Moreover, the result indicates that PSO-FSSVM can obtain higher classification accuracy than GA-FSSVM classification for hyperspectral data.

Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

The performance of the support vector machine models depends on a proper setting of its parameters to a great extent.A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed.A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines.The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine,and the precision and reliability of the fault classification results can meet the requirement of practical application.It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.

Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting

Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.

A Transductive Support Vector Machine Algorithm Based on Ant Colony Optimization

Transductive support vector machine optimization problem is a NP problem, in the case of larger number of labeled samples, it is often difficult to obtain a global optimal solution, thereby the good generalization ability of transductive learning has been affected. Previous methods can not give consideration to both running efficiency and classification precision. In this paper, a transductive support vector machine algorithm based on ant colony optimization is proposed to overcome the drawbacks of the previous methods. The proposed algorithm approaches the approximate optimal solution of Transductive support vector machine optimization problem by ant colony optimization algorithm, and the advantage of transductive learning can be fully demonstrated. Experiments on several UCI standard datasets and the newsgroups 20 dataset showed that, with respect to running time and classification precision, the proposed algorithm has obvious advantage over the previous algorithms.

New approach to training support vector machine

Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

Fuzzy least squares support vector machine soft measurement model based on adaptive mutative scale chaos immune algorithm

In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong coupling and large time-delay,in terms of the fuzzy character of this real complex system,a fuzzy least squares support vector machine(FLS-SVM) soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune algorithm.The simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and robustness.And application results show that the relative errors of the soft measurement model are less than 3.34%.