Parallel Support Vector Machine Research Articles

Optimization of parallel SVM algorithm for big data

Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy.

Application of support vector machine system introducing multiple submodels in data mining

As the information age develops, the scale and form of data become more diverse and diverse. Therefore, people need to use effective means to process information. For large-scale data mining problems, a clustering-based kernel matrix inner product filtering method is introduced to decompose the original quadratic programming problem into multiple sub-problems to support parallel training. And a Spark-based multiple submodels parallel support vector machine is proposed. By introducing open-source tools such as OpenCV, image feature extraction can be performed on large-scale video data. Finally, combined with the designed parallel support vector machine algorithm, video facial and expression recognition is carried out. These experiments confirmed that the research method achieved a maximum acceleration ratio of 2090 times when processing Covtype datasets. The research model could achieve an accuracy of over 99 %. Under the maximum data scale experiment, the research model improved prediction accuracy by 21 percentage points with an acceptable additional time cost of about 4 min only. Task parallel processing could more fully utilize cluster performance, increasing by approximately 3.5 m/s2 from 30 to 150 cores. The research model had the highest recognition accuracy for facial expressions, further demonstrating the effectiveness and superiority of this method. The research method has improved the efficiency of big data analysis and mining, and is of great significance in parallel analysis of video data.

Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods.

Hyperspectral imaging (HSI) has become a very compelling technique in different scientific areas; indeed, many researchers use it in the fields of remote sensing, agriculture, forensics, and medicine. In the latter, HSI plays a crucial role as a diagnostic support and for surgery guidance. However, the computational effort in elaborating hyperspectral data is not trivial. Furthermore, the demand for detecting diseases in a short time is undeniable. In this paper, we take up this challenge by parallelizing three machine-learning methods among those that are the most intensively used: Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) algorithms using the Compute Unified Device Architecture (CUDA) to accelerate the classification of hyperspectral skin cancer images. They all showed a good performance in HS image classification, in particular when the size of the dataset is limited, as demonstrated in the literature. We illustrate the parallelization techniques adopted for each approach, highlighting the suitability of Graphical Processing Units (GPUs) to this aim. Experimental results show that parallel SVM and XGB algorithms significantly improve the classification times in comparison with their serial counterparts.

Determination of microplastics by FTIR spectroscopy based on quaternion parallel feature fusion and support vector machine

This study proposed a FTIR spectroscopy combing with quaternion parallel feature extraction methodology for the first time to address the qualitative and quantitative analysis of microplastics. For the international available microplastic FTIR dataset, the original, the first derivative and the second derivative FTIR spectra of polyethylene(PE), PE + fouling and polypropylene(PP) samples were first formed as an integral representation by a pure quaternion matrix. Then quaternion principal component analysis(QPCA) was conducted to obtain quaternion feature vectors and support vector machine(SVM) was followed to establish the qualitative model. Meanwhile, we experimentally mixed four types of microplastics of polyvinyl chloride(PVC), polystyrene (PS), PP and PE into sand to prepare simulated samples and collected their FTIR spectra. The concentrations of microplastics were 0.5%, 1%, 1.5%, 3%, 5%, 10%, respectively. The QPCA based parallel feature extraction combing with SVR was conducted to establish the quantitative model. The results show that QPCA has transcended traditional PCA and serial feature extraction methods. Furthermore, compared with traditional algorithms of PLS, BPNN, RF, and 1D-CNN, QPCA-SVM has achieved the highest values of accuracy, precision, recall and F1-score, which were 0.98571, 1, 0.99412 and 0.99697, respectively. QPCA-SVR has achieved the smallest RMSEP for four kinds of microplastics, which were 0.26361, 0.80558, 0.37099, and 0.16246, respectively. This work demonstrates QPCA fuses more spectral characteristics and reduces the redundancy of the complex vector, which is beneficial to improve the recognition and regression ability of SVM. The “QPCA enabled FTIR spectroscopy technique” in this study provides a new idea to promote the rapid and accurate determination of microplastics.

Correlation analysis based relevant variable selection for wind turbine condition monitoring and fault diagnosis

Wind turbines’ fault diagnosis under complex environments and disturbances is significant to maintaining high reliability and secure operation over a prolonged period of time. Due to the difficulty of installing additional sensors, the supervisory control and data acquisition system is the only path for condition monitoring and fault diagnosis. However, the complexity of numerous variables bogged down the situation of diagnosis. Hence, this paper proposes a correlation analysis method to filter the variables for maximizing redundant data suppression first. Secondly, a data utility maximization method based on a prior-posterior support vector machine is proposed. Finally, a series of parallel support vector machines are used to realize multi-condition monitoring and fault diagnosis. Experiment results illustrate the effectiveness, robustness, and generality of the method.

Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization

Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization method. The final contribution is to improve the efficiency of the HHO-SVM by adopting a parallel approach that employs the data distribution. The proposed models are evaluated using the Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The results show that the HHO-SVM achieves a 98.24% accuracy rate with the normalization scaling technique, outperforming other related works. On the other hand, the HHO-SVM achieves a 99.47% accuracy rate with the equilibration scaling technique, which is better than other previous works. Finally, to compare the three effective scaling strategies on four CPU cores, the parallel version of the proposed model provides an acceleration of 3.97.

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.

Intelligent recognition method of laser image based on big data analysis technology

In order to improve the recognition effect of laser images, this study designed an intelligent recognition method of laser images based on big data analysis technology. On the basis of setting up the laser holographic scanning device and parameters, the laser image is obtained by using the calibration method of vision system. In order to avoid the limitation of coordinate system in the process of laser image recognition, a rational function model with general attributes is constructed. Then, convolutional neural network is used to output the feature data of laser images, and Spark parallel support vector machine algorithm is used to complete the classification of laser images. Finally, the SVM classification model based on the big data analysis technology is constructed. The texture feature data can be input to quickly output the classification results of laser images, and then the intelligent classification and recognition of laser images can be realized according to the probability distribution. Experimental results show that this method can accurately identify the tiny features in laser images, and the recognition results have high peak signal-to-noise ratio and high recognition accuracy.

Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios.

Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded.

Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge

PurposeThis paper aims to propose the new incremental and parallel training algorithm of proximal support vector machines (Inc-Par-PSVM) tailored on the edge device (i.e. the Jetson Nano) to handle the large-scale ImageNet challenging problem.Design/methodology/approachThe Inc-Par-PSVM trains in the incremental and parallel manner ensemble binary PSVM classifiers used for the One-Versus-All multiclass strategy on the Jetson Nano. The binary PSVM model is the average in bagged binary PSVM models built in undersampling training data block.FindingsThe empirical test results on the ImageNet data set show that the Inc-Par-PSVM algorithm with the Jetson Nano (Quad-core ARM A57 @ 1.43 GHz, 128-core NVIDIA Maxwell architecture-based graphics processing unit, 4 GB RAM) is faster and more accurate than the state-of-the-art linear SVM algorithm run on a PC [Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32 GB RAM].Originality/valueThe new incremental and parallel PSVM algorithm tailored on the Jetson Nano is able to efficiently handle the large-scale ImageNet challenge with 1.2 million images and 1,000 classes.

Hyperspectral Image Classification Based on Non-Parallel Support Vector Machine

Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model adds an additional empirical risk minimization term to the original optimization problem by adding the least square term of the sample and obtains two non-parallel hyperplanes, respectively, forming a new non-parallel SVM algorithm to minimize the additional empirical risk of non-parallel SVM (Additional Empirical Risk Minimization Non-parallel Support Vector Machine, AERM-NPSVM). On the basis of AERM-NPSVM, the bias constraint is added to it, and AERM-NPSVM (BC-AERM-NPSVM) is further obtained. The experimental results show that, compared with the traditional parallel SVM model and the classical non-parallel SVM model, Twin Support Vector Machine (TWSVM), the new model, has a better effect in hyperspectral image classification and better generalization performance.

Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault Diagnosis.

A new method of multi-sensor signal analysis for fault diagnosis of centrifugal pump based on parallel factor analysis (PARAFAC) and support vector machine (SVM) is proposed. The single-channel vibration signal is analyzed by Continuous Wavelet Transform (CWT) to construct the time–frequency representation. The multiple time–frequency data are used to construct the three-dimension data matrix. The 3-level PARAFAC method is proposed to decompose the data matrix to obtain the six features, which are the time domain signal (mode 3) and frequency domain signal (mode 2) of each level within the three-level PARAFAC. The eighteen features from three direction vibration signals are used to test the data processing capability of the algorithm models by the comparison among the CWT-PARAFAC-IPSO-SVM, WPA-PSO-SVM, WPA-IPSO-SVM, and CWT-PARAFAC-PSO-SVM. The results show that the multi-channel three-level data decomposition with PARAFAC has better performance than WPT. The improved particle swarm optimization (IPSO) has a great improvement in the complexity of the optimization structure and running time compared to the conventional particle swarm optimization (PSO.) It verifies that the proposed CWT-PARAFAC-IPSO-SVM is the most optimal hybrid algorithm. Further, it is characteristic of its robust and reliable superiority to process the multiple sources of big data in continuous condition monitoring in the large-scale mechanical system.

A 65nm/0.448 mW EEG processor with parallel architecture SVM and lifting wavelet transform for high-performance and low-power epilepsy detection

In recent years, low-power and wearable biomedical testing devices have emerged as a key answer to the challenges associated with epilepsy disorders, which are prone to crises and require prolonged monitoring. The feature vector of the electroencephalographic (EEG) signal was extracted using the lifting wavelet transform algorithm, and the hardware of the lifting wavelet transform module was optimized using the canonic signed digit (CSD) coding method. A low-power EEG feature extraction circuit with a power consumption of 0.42 mW was constructed. This article employs the support vector machine (SVM) technique after feature extraction to categorize and identify epilepsy. A parallel SVM processing unit was constructed to accelerate classification and identification, and then a high-speed, low-power EEG epilepsy detection processor was implemented. The processor design was completed using TSMC 65 nm technology. The chip size is 0.98 mm2, operating voltage is 1 V, operating frequency is 1 MHz, epilepsy detection latency is 0.91 s, power consumption is 0.448 mW, and energy efficiency of a single classification is 2.23 μJ/class. The CHB-MIT database test results show that this processor has a sensitivity of 91.86% and a false detection rate of 0.17/h. Compared to other processors, this processor is more suitable for portable/wearable devices.

Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

Efficient feature subset selection and classification using levy flight‐based cuckoo search optimization with parallel support vector machine for the breast cancer data

AbstractThe complexity of breast cancer classification is a drastic concern for a long time. Therefore, different algorithms for data mining have been developed and used to effectively classify the dataset of breast cancer. But, due to inefficient subset choice of this operation, these algorithms have problems in accuracy of classification. In this article, levy flight–based cuckoo search optimization (LFCSO) with parallel support vector machine (PSVM) is proposed for enhancing the classification accuracy of breast cancer. The proposed LFCSO‐PSVM technique includes three key steps: (i) preprocessing, (ii) feature selecting subset, and (iii) classification. Preprocessing is carried out using k‐means clustering that is required to eliminate the values. To acquire maximum detailed feature, these attributes are transmitted to the process the feature selecting subset, which is executed by LFCSO algorithm, and the aspiration function is used to determine the essential feature on the basis of proper fitness values. Then, the PSVM algorithm is implemented to classify the model for training and testing. This is used to increase the accuracy of the classification for the breast cancer dataset. The experimental outcomes demonstrate that the proposed LFCSO‐PSVM algorithm provides higher efficiency of classification than the existing algorithms in terms of precision, recall, f‐measurement, and reliability.

Research on Parallel Support Vector Machine Based on Spark Big Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

Parallel SVM model for forest fire prediction

Forest fire is considered as one of the main cause of the environmental hazard that provides many negative effects. Effective Forest Fire prediction models help to take the necessary steps to prevent forest fire and its negative effects. Existing methods of Cascade Correlation Network (CCN), Radial Basis Function (RBF) and Support Vector Machine (SVM) were applied for the forest fire prediction. Existing methods have the limitations of over fitting problems and lower efficiency in prediction. Existing methods in forest fire prediction have lower efficiency in large dataset due to overfitting problem in the models. The parallel SVM method is developed in this research for reliable performance of the Forest Fire Prediction. Conventional SVM has a higher efficiency in predicting the small fire and has lower efficiency in predicting large fire. The SPARK and PySpark were applied to perform the data segmentation and feature selection in the prediction process. A parallel SVM model is developed to train the meteorological data and predict the forest fire effectively. The parallel SVM model reduces the computational time and high storage required for the analysis. Parallel SVM considers the Forecast Weather Index (FWI) and some weather parameters for the prediction of a forest fire. The parallel SVM model is evaluated on the Indian and Portugal data to analyze the efficiency of the model. The parallel SVM model has the 63.45 RMSE and SVM method has 63.5 RMSE in the Portugal data.

Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams.

Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy consumption and minimizing data exchange between devices in different zones. This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs), based on ensembles, tailored to these kinds of problems. To achieve this, the training set is split into several Voronoi regions. These regions are small enough to permit faster parallel training of SVMs, reducing computational payload. Results from experiments comparing the proposed method with a single SVM and a standard ensemble of SVMs demonstrate that this approach can provide comparable performance while limiting the number of regions required to solve classification tasks. These advantages facilitate the development of energy-efficient policies in WSN.

Identification of facial expression using a multiple impression feedback recognition model

Facial expression recognition (FER) plays a vital role in the automatic detection of human emotions with intelligent machine. Since the FER is an interdisciplinary technique involving biology, computer science and even psychology, more challenges will be encountered as we pursue a high recognition accuracy of facial expressions. Inspired by the progressive enhancing procedure of face recognition of human, we proposed a multiple impression feedback recognition model (MIFR) for the identification of facial expression. Different from current deep learning techniques, the MIFR realizes a quick facial expression recognition through cascade feedback recognition cycles. Multiple impression features of an FER image are firstly obtained by the discrete wavelet decomposition (DWT). Each recognition cycle is implemented by inputting wavelet impression features in a specific decomposition scale into a classifier set of parallel Support Vector Machines (SVMs). In terms of coarse-to-fine wavelet features on multiple scales, the recognition results are gradually improved through integrating classification probability vectors of multiple cycles. In order to validate the performance of the MIFR_SVM for face expression recognition, we also conducted an experiment of facial multi-view expression with occlusion (FMEO) in the laboratory. Three traditional schemes and seven deep learning schemes have been chosen for the FER comparison of three public datasets and one lab dataset FMEO. The classification results show that the MIFR_SVM is not only superior than traditional schemes, but also performs better than deep learning techniques, including both neural-network-based ones and random-forest-based ones. The average recognition accuracy of MIFR_SVM reaches to 92.31% for four datasets. Furthermore, the MIFR_SVM has fewer parameters, less complexity and adaptable cascade structure, which is more suitable for the image datasets with small size or with diverse qualities.

Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.