Traditional Support Vector Machine Research Articles

Multi-objective diesel engine emission management and control technology based on SVM and NSGA-II

To improve the emission control effect of diesel engines, a pre-emission model for diesel engines using biofuels is constructed using support vector machine. Considering that traditional support vector machine training relies on initial parameters and is prone to local convergence, a genetic algorithm model is adopted to construct an improved diesel engine pre-emission model. At the same time, with the optimization goal of diesel engine emissions of particles and gases, the improved diesel engine pre-emission model is used to obtain emission data as training parameters. The second generation non-dominated genetic algorithm is introduced to construct a multi-objective diesel engine emission control model, achieving optimal control of diesel engine emissions. In the experimental analysis of diesel engine pre emission models, the proposed pre emission model has a better prediction effect in nitrogen oxides, with an average prediction accuracy of 0.986, which is better than the Particle Swarm Optimization-Support Vector Machine model's 0.906, and is more accurate in detecting emissions. Simultaneously predicting particulate matter, the average prediction accuracy of the proposed model is 0.978, while the prediction accuracy of the Particle Swarm Optimization-Support Vector Machine model is 0.902, indicating that the overall prediction effect of the proposed method is better. In the experimental analysis of multi-objective optimization models, the proposed multi-objective optimization model performs best in diesel emission optimization. The proposed model can achieve convergence in the shortest time, and the minimum nitrogen oxide emissions are 210.7 ppm. Compared with Non-dominated Sorting Genetic Algorithm II and Multi-Objective Particle Swarm Optimization models, the particle emission optimization effect is improved by 23.5 % and 18.6 %. From this, it can be seen that the proposed method has excellent performance in diesel engine emission monitoring and pollutant optimization, surpassing related technologies in the same period. The research technology provides important technical references for the monitoring and emission control of harmful substances in diesel engines.

Text Classification from positive and unlabeled examples using Support Vector Machine (SVM)

Support Vector Machines (SVMs) are a powerful machine learning algorithm that can be used for text classification. Traditional SVMs require both positive and negative examples to train the model. However, in many real-world scenarios, it can be difficult or expensive to obtain negative examples. This study explores the application of SVMs in textclassification when only positive and unlabeled examples are available. Theresults showed that the proposed approach achieved competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. The utilization of SVC in the proposed approach is twofold. First, the SVC model is used to classify theunlabeled examples as positive or negative. Second, the SVC model is used to select the positive examples that are added to the training set. Thisiterative process of training and selecting examples helps to improve the classification accuracy of the SVM model. The proposed approach is a promising method for text classification when only positive and unlabeled examples are available. Theapproach is effective in achieving competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. This work contributes to enhancing text classification techniques, particularly in situationswith resource constraints and challenging label acquisition. Keywords: Support Vector Machine(S VM), Text Classifications ,Text Mining, SVC, Supervised Methods

A classification method of fuzzy semi-supervised support vector machines for the problems of imbalance

Positive instances are often significantly less than negative instances in real-world classification problems. However, positive categories are typically more relevant to the primary focus of categorization tasks. Moreover, obtaining labeled data is often expensive, and the majority of real-life data is unlabeled. Therefore, semi-supervised learning has become a popular approach for addressing imbalanced problems. Traditional support vector machines (SVMs) treat all samples equally and are not suitable for semi-supervised learning. To address this issue, a semi-supervised model called the fuzzy semi-supervised SVM (FS3VM) has been proposed. The FS3VM model uses the degree of entropy-based fuzzy membership to ensure the materiality of positive classes by assigning positive instances to relatively large degrees of fuzzy membership. After introducing the mainstream FS3VM model, the fundamental theory and methods of the model are discussed and expanded upon, including the FS3VM algorithm, which applies the Sequential Minimal Optimization (SMO) algorithm to the dual problem. The proposed FS3VM model is a smooth and continuous optimization problem, and its dual is a standard quadratic programming. Experimental results demonstrate that the proposed FS3VM model outperforms other compared learning algorithms.

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data.

The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.

Uncertain support vector machine based on uncertain set theory

Support vector machine (SVM) is a supervised binary classifier with good generalization ability and excellent computational properties. It has been widely used in many fields such as image recognition, bioinformatics and so on. However, the traditional SVM requires the input data to be clear and knowable, while in the actual application process, there will be many cases that the input data is uncertain. In order to solve this problem, a new SVM model is proposed in this paper by combining the uncertainty theory with the SVM theory. The uncertainty theory was proposed by Liu in 2007, and it is often used to describe the uncertainty of things. The uncertain set in uncertainty theory is often used to model unsharp concepts. Therefore, this paper regards each uncertain data as an uncertain set and establishes a SVM model with uncertain chance constraints. However, the uncertain chance constraints are non-convex. Therefore, this paper gives the equivalent transformation process of constraint conditions when the input data are triangular uncertain sets. Finally, the non-convex constraint conditions are transformed into the linear constraint conditions, so that the model is transformed into a nonlinear programming model. In the numerical experiment, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, which proves the feasibility of the model.

Prediction and analysis of time series data based on granular computing.

The advent of the Big Data era and the rapid development of the Internet of Things have led to a dramatic increase in the amount of data from various time series. How to classify, correlation rule mining and prediction of these large-sample time series data has a crucial role. However, due to the characteristics of high dimensionality, large data volume and transmission lag of sensor data, large sample time series data are affected by multiple factors and have complex characteristics such as multi-scale, non-linearity and burstiness. Traditional time series prediction methods are no longer applicable to the study of large sample time series data. Granular computing has unique advantages in dealing with continuous and complex data, and can compensate for the limitations of traditional support vector machines in dealing with large sample data. Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed, and the basic principles of traditional time series forecasting methods and granular computing are investigated. Secondly, in terms of predicting the trend of data changes, it is proposed to apply the fuzzy granulation algorithm to first convert the sample data into coarser granules. Then, it is combined with a support vector machine to predict the range of change of continuous time series data over a period of time. The results of the simulation experiments show that the proposed model is able to make accurate predictions of the range of data changes in future time periods. Compared with other prediction models, the proposed model reduces the complexity of the samples and improves the prediction accuracy.

A parallel and balanced SVM algorithm on spark for data-intensive computing

Support Vector Machine (SVM) is a machine learning with excellent classification performance, which has been widely used in various fields such as data mining, text classification, face recognition and etc. However, when data volume scales to a certain level, the computational time becomes too long and the efficiency becomes low. To address this issue, we propose a parallel balanced SVM algorithm based on Spark, named PB-SVM, which is optimized on the basis of the traditional Cascade SVM algorithm. PB-SVM contains three parts, i.e., Clustering Equal Division, Balancing Shuffle and Iteration Termination, which solves the problems of data skew of Cascade SVM and the large difference between local support vector and global support vector. We implement PB-SVM in AliCloud Spark distributed cluster with five kinds of public datasets. Our experimental results show that in the two-classification test on the dataset covtype, compared with MLlib-SVM and Cascade SVM on Spark, PB-SVM improves efficiency by 38.9% and 75.4%, and the accuracy is improved by 7.16% and 8.38%. Moreover, in the multi-classification test, compared with Cascade SVM on Spark on the dataset covtype, PB-SVM improves efficiency and accuracy by 94.8% and 18.26% respectively.

Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models

The evaluation of land comprehensive carrying capacity (LCCC) is a popular topic in regional sustainable development and land science research. However, these evaluations are often not objective due to the complexity and nonlinear characteristics of the LCCC evaluation indicators. This study provides a new framework for using machine learning methods to objectively evaluate LCCC. The Driver-Pressure-State-Impact-Response (DPSIR) indicator conceptual framework was used for the spatial visualisation of indicators and was then combined with the random forest (RF) model to optimise the LCCC evaluation. The results showed the following: (1) The accuracy of the integrated RF and DPSIR model was better than that of traditional support vector machine (SVM) and principal component analysis (PCA) methods, indicating that RF model was more suitable for LCCC evaluations. (2) The contribution of the DPSIR indicators after RF optimisation was significantly different from that of the traditional analytic hierarchy process (AHP), the contribution of the influence subsystem (I) (48.7%) was enhanced, while the contribution of the drive subsystem (D) (6.8%) was weakened, demonstrating that after RF optimisation, the DPSIR was more conducive for handling complex nonlinear systems and objectively reflecting indicator contributions. (3) The objective attributes of the DPSIR indicators were optimised through the processing of spatial visualisation models, indicating that RF was suitable for processing evaluations with a multidimensional spatial heterogeneity index system. The LCCC evaluation model can be applied to other carrying capacity case studies and operational processes and provide a scientific method for handling the multidimensional nonlinear evaluation index system and objective evaluation of LCCC.

Capped L2,p-Norm Metric Based on Robust Twin Support Vector Machine with Welsch Loss

A twin bounded support vector machine (TBSVM) is a phenomenon of symmetry that improves the performance of the traditional support vector machine classification algorithm. In this paper, we propose an improved model based on a TBSVM, called a Welsch loss with capped L2,p-norm distance metric robust twin bounded support vector machine (WCTBSVM). On the one hand, by introducing the capped L2,p-norm metric in the TBSVM, the problem of the non-sparse output of the regularization term is solved; thus, the generalization and robustness of the TBSVM is improved and the principle of minimizing the structural risk is realized. On the other hand, a bounded, smooth, and non-convex Welsch loss function is introduced to reduce the influence of noise, which further improves the classification performance of the TBSVM. We use a half-quadratic programming algorithm to solve the model non-convexity problem caused by Welsch loss. Therefore, the WCTBSVM is more robust and effective in dealing with noise compared to the TBSVM. In addition, to reduce the time complexity and speed up the convergence of the algorithm, we constructed a least squares version of the WCTBSVM, named the fast WCTBSVM (FWCTBSVM). Experimental results on both UCI and artificial datasets show that our model can show better classification performance on classification problems.

Identification of overhead line fault traveling wave and interference clutter based on convolution neural network and random forest fusion

High-speed traveling wave acquisition devices often use a mutation start algorithm with a low threshold value, which can collect a large number of interference clutters. If the devices automatically screen out a flashover fault traveling wave from a massive traveling wave, the traveling wave fault location performance could be improved. In this paper, a fault and interference classification method based on the random forest algorithm, which uses a convolutional neural network as a supervised feature extractor of traveling wave data, is proposed. First, one-dimensional traveling wave data are mapped to a two-dimensional matrix by dividing the information section, and a gray image is used to characterize it. Next, a two-dimensional convolution neural model of traveling wave data is constructed to realize the self-learning of traveling wave data characteristics, and the waveform feature sequence of traveling wave data is obtained. Then, on this basis, a random forest algorithm is used to realize automatic identification and screening of flashover fault traveling waves. Finally, a large number of tests on the simulation and measured data show that the proposed combined algorithm based on the feature extraction and classification of stochastic forest algorithm has better recognition effect of fault and interference than the traditional support vector machine, single stochastic forest algorithm, and convolutional neural network.

Research on eye movement data classification using support vector machine with improved whale optimization algorithm

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

Gene splicing site recognition is a very important research topic in smart healthcare. Gene splicing site recognition is of great significance, not only for the large-scale and high-quality computational annotation of genomes but also for the analysis and recognition of the gene sequences evolutionary process. It is urgent to study a reliable and effective algorithm for gene splice site recognition. Traditional Twin Support Vector Machine (TWSVM) algorithm has advantages in solving small-sample, nonlinear, and high-dimensional problems, but it cannot deal with parameter selection well. To avoid the blindness of parameter selection, particle swarm optimization algorithm was used to find the optimal parameters of twin support vector machine. Therefore, a Particle Swarm Optimization Twin Support Vector Machine (PSO-TWSVM) algorithm for gene splicing site recognition was proposed in this paper. The proposed algorithm was compared with traditional Support Vector Machine algorithm, TWSVM algorithm, and Least Squares Support Vector Machine algorithm. The comparison results show that the positive sample recognition rate, negative sample recognition rate, and correlation coefficient (CC) of the proposed algorithm are the best among the four different support vector machine algorithms. The proposed algorithm effectively improves the recognition rate and the accuracy of splice sites. The comparison experiments verify the feasibility of the proposed algorithm.

A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition

Human activity recognition (HAR) is crucial to infer the activities of human beings, and to provide support in various aspects such as monitoring, alerting, and security. Distinct activities may possess similar movements that need to be further distinguished using contextual information. In this paper, we extract features for context-aware HAR using a convolutional neural network (CNN). Instead of a traditional CNN, a combined 3D-CNN, 2D-CNN, and 1D-CNN was designed to enhance the effectiveness of the feature extraction. Regarding the classification model, a weighted twin support vector machine (WTSVM) was used, which had advantages in reducing the computational cost in a high-dimensional environment compared to a traditional support vector machine. A performance evaluation showed that the proposed algorithm achieves an average training accuracy of 98.3% using 5-fold cross-validation. Ablation studies analyzed the contributions of the individual components of the 3D-CNN, the 2D-CNN, the 1D-CNN, the weighted samples of the SVM, and the twin strategy of solving two hyperplanes. The corresponding improvements in the average training accuracy of these five components were 6.27%, 4.13%, 2.40%, 2.29%, and 3.26%, respectively.

Effect of Local Network Characteristics on the Performance of the SSVEP Brain-Computer Interface

ObjectiveFor decades, a great deal of interest in investigating brain network functional connective features has arisen in brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). Traditional decoding algorithms, for example, canonical correlation analysis (CCA), only consider the inherent properties of each channel in terms of feature extraction for the single channel electroencephalogram (EEG) signal, with inadequate features that cannot fully utilize the information transmitted by the brain. Material and methodsThis paper proposes a fused feature extraction method, CCA-DTF, which combines CCA with a direct transfer function (DTF) to construct local brain network features with seven leads in the occipital region. First, the features extracted by the CCA algorithm were combined with these features extracted by DTF to analyze the EEG data from 20 subjects. Then, two methods, support vector machine (SVM) and random forest (RF), were used in constructing the classifiers for the four tasks classification of the SSVEP-BCI. ResultsThe experimental results showed that incorporating local network features (extracted from DTF or Pearson correlation coefficient) can effectively improve the classification average accuracy and the information transfer rate (ITR) of SSVEP, not only for SVM but also for the ensemble method RF. In particular, CCA-DTF plus SVM obtained a 94.52% classification average accuracy and a 49.23 bits/min ITR in a time window of 2 seconds. The performance was 5.57% and 8.01 bits/min higher than those of traditional CCA plus SVM, respectively. ConclusionThe proposed feature extraction method based on local network features is robust for improving the performance of SSVEP-BCI significantly, which has a perspective of being used in neural rehabilitation engineering field.

A combination of machine learning based natural language processing with technical analysis for stock trading

Stock price analysis appropriately is a challenging area of research as many factors directly affect stock prices. As a result, so not easy to analyze to identify stock trading signals appropriately. The proposed approach builds a framework for classifying stock trading signals by combining natural language processing with technical analysis. The dataset implemented focuses on corporate news and stock indicators from 01-01-2019 to 31-12-2021 from the eight corporates of the Thai Industry Group Index and Sector Index. Two traditional machine learning models, multilayer perceptron (MLP) and support vector machine (SVM), and four deep learning models, Bidirectional GRU (BiGRU), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and long short-term memory (LSTM) used for comparison purposes. The training model classifies daily trading signals into three classes: buy, sell, and hold-after that, the model’s efficiency evaluates by measuring accuracy, precision, recall, and F1-score. For the results, classification average efficiency in all models showed that the BiGRU model obtained higher average accuracy (0.93), precision (0.93), recall (0.93), and F1-score (0.92) than other models. Therefore, the BiGRU model was appropriate for our experiment and was applied to determine daily trading signals for analyzing investment returns.

Identifying autism spectrum disorder using edge-centric functional connectivity.

Brain network analysis is an effective method to seek abnormalities in functional interactions for brain disorders such as autism spectrum disorder (ASD). Traditional studies of brain networks focus on the node-centric functional connectivity (nFC), ignoring interactions of edges to miss much information that facilitates diagnostic decisions. In this study, we present a protocol based on an edge-centric functional connectivity (eFC) approach, which significantly improves classification performance by utilizing the co-fluctuations information between the edges of brain regions compared with nFC to build the classification mode for ASD using the multi-site dataset Autism Brain Imaging Data Exchange I (ABIDE I). Our model results show that even using the traditional machine-learning classifier support vector machine (SVM) on the challenging ABIDE I dataset, relatively high performance is achieved: 96.41% of accuracy, 98.30% of sensitivity, and 94.25% of specificity. These promising results suggest that the eFC can be used to build a reliable machine-learning framework to diagnose mental disorders such as ASD and promote identifications of stable and effective biomarkers. This study provides an essential complementary perspective for understanding the neural mechanisms of ASD and may facilitate future investigations on early diagnosis of neuropsychiatric disorders.

An improved model for building energy consumption prediction based on time-series analysis

The development and popularisation of renewable energy is necessary. The application of renewable energy technology in buildings is an important research direction. Moreover, the prediction of renewable energy consumption in this direction is an essential research content. In view of this, a building energy consumption prediction model of renewable energy based on time-series analysis and a support vector machine (SVM) is proposed. The performance test of this model showed that its loss value was as low as 1.5% in the training set, and the loss value was 4.1% in the test set. In addition, it showed the highest accuracy rate of 95.5% in the neural network accuracy test, which is significantly higher than that of traditional algorithms. About the overall energy consumption prediction ability of the model, the experimental results showed that the lowest error of the energy consumption prediction model was 2.3%, the average relative error of the traditional SVM model in the same data set was 6.8% and that of the chaotic time-series model was 4.1%. Compared with the prediction ability of the traditional models currently used, the prediction ability of the energy consumption prediction model has been greatly improved, and it has the potential to be put into practical application.

Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning

Hydrothermal biochar is a promising sustainable soil remediation agent for plant growth. Demands for biochar properties differ due to the diversity of soil environment. In order to achieve accurate biochar properties prediction and overcome the interpretability bottleneck of machine learning models, this study established a series of data-enhanced machine learning models and conducted relevant sensitivity analysis. Compared with traditional support vector machine, artificial neural network, and random forest models, the accuracy after data enhancement increased in average from 5.8% to 15.8%, where the optimal random forest model showed the average of accuracy was 94.89%. According to sensitivity analysis results, the essential factors influencing the predicting results of the models were reaction temperature, reaction pressure, and specific element of biomass feedstock. As a result, data-enhanced interpretable machine learning proved promising for the characteristics prediction of hydrothermal biochar.

A Relief-PGS algorithm for feature selection and data classification

As a supervised learning algorithm, Support Vector Machine (SVM) is very popularly used for classification. However, the traditional SVM is error-prone because of easy to fall into local optimal solution. To overcome the problem, a new SVM algorithm based on Relief algorithm and particle swarm optimization-genetic algorithm (Relief-PGS) is proposed for feature selection and data classification, where the penalty factor and kernel function of SVM and the extracted feature of Relief algorithm are encoded as the particles of particle swarm optimization-genetic algorithm (PSO-GA) and optimized by iteratively searching for optimal subset of features. To evaluate the quality of features, Relief algorithm is used to screen the feature set to reduce the irrelevant features and effectively select the feature subset from multiple attributes. The advantage of Relief-PGS algorithm is that it can optimize both feature subset selection and SVM parameters including the penalty factor and the kernel parameter simultaneously. Numerical experimental results indicated that the classification accuracy and efficiency of Relief-PGS are superior to those of other algorithms including traditional SVM, PSO-GA-SVM, Relief-SVM, ACO-SVM, etc.

Study on the methods of feature extraction based on electromyographic signal classification.

Electromyography (EMG) is a form of biological information, which is used in many fields to help people study human muscle movement, especially in the study of bionic hands. EMG signals can be used to explain the activity at a certain moment through the signal changes of human muscles, and it is a very complex signal, so processing it is very important. The process of EMG signals can be divided into acquisition, pre-processing, feature extraction, and classification. Not all signal channels are useful in EMG acquisition, and it is important to select useful signals among them. Therefore, this study proposes a feature extraction method to extract the most representative two-channel signals from the eight-channel signals. In this paper, the traditional principal component analysis method and support vector machine feature elimination are used to extract signal channels. At the same time, a new method, correlation heat map, is proposed to implement feature extraction method by using three methods, and three classification algorithms of K-nearest neighbor, random forest, and support vector machine are used to verify. The results show that the classification accuracy of the proposed method is better than that of the other two traditional methods.