Support Vector Machine Ensemble Algorithm Research Articles

Early warning model based on support vector machine ensemble algorithm

Support Vector Machine (SVM) is a powerful machine learning technique often applied in various early warning scenarios. When using SVM for binary classification, its output results can be converted into a probability distribution based on sample categories. Through the analysis of the results, it is found that when the probability of a sample belonging to a positive class is equivalent to the probability of a sample belonging to a negative class, there is a high possibility of misclassification of the sample. To address this issue, this study proposes a new SVM ensemble model construction method. We generate SVM sub-classifiers to extract error prone set, and utilise three different combination rules to construct ensemble model based on the output probability of the samples. The accuracy of the whole data set is improved for the classification accuracy of the easily misclassified data are improved. This research evaluated the model based on Wind turbine SCADA data and UCI benchmark datasets. According to the research results, the accuracy rate of SCADA data is as high as 97.52%, and the accuracy rate of German credit data is 77.50%. Meanwhile, the comparison with other methods also validates the effectiveness of the proposed method.

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.

Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

Mild cognitive impairment (MCI) as the potential sign of serious cognitive decline could be divided into two stages, i.e., late MCI (LMCI) and early MCI (EMCI). Although the different cognitive states in the MCI progression have been clinically defined, effective and accurate identification of differences in neuroimaging data between these stages still needs to be further studied. In this paper, a new method of clustering-evolutionary weighted support vector machine ensemble (CEWSVME) is presented to investigate the alterations from cognitively normal (CN) to EMCI to LMCI. The CEWSVME mainly includes two steps. The first step is to build multiple SVM classifiers by randomly selecting samples and features. The second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs, thereby improving the final classification performances. Additionally, we extracted the optimal features to detect the differential brain regions in MCI progression, and confirmed that these differential brain regions changed dynamically with the development of MCI. More exactly, this study found that some brain regions only have durative effects on MCI progression, such as parahippocampal gyrus, posterior cingulate gyrus and amygdala, while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the progression. Our work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.

An Effective SVM Ensemble Algorithm Based on Different Thresholds of PCA

This paper proposes an effective ensemble classifier, named PCAenSVM, which consists of ten weak Support Vector Machine classifiers based on different Principal Component Analysis thresholds. Those ten base Support Vector Machine classifiers are made up to fulfill classification tasks using Majority Voting strategy. Experiments are made on four UCI data sets and a data set from the Uppsala University to evaluate the performances of PCAenSVM. The results of PCAenSVM are compared with that of LibSVM and EnsembleSVM. Experimental results show that PCAenSVM has better classification accuracy than other two algorithms. Moreover, PCAenSVM has the same confidence level with the LibSVM, and its confidences of accuracy and sensitivity on those five data sets outperform that of the EnsembleSVM.

PEnSVM: An Improved SVM Ensemble Algorithm Based on Different PCA Thresholds

PEnSVM: An Improved SVM Ensemble Algorithm Based on Different PCA Thresholds

A Wavelet Transform Based Support Vector Machine Ensemble Algorithm and Its Application in Network Intrusion Detection

A Wavelet Transform Based Support Vector Machine Ensemble Algorithm and Its Application in Network Intrusion Detection

PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM

As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier. This paper proposes a new ensemble algorithm based on SVM. The proposed classification algorithm PB-SVM Ensemble consists of some SVM classifiers produced by PCAenSVM and fifty classifiers trained using Bagging, the results are combined to make the final decision on testing set using majority voting. The performance of PB-SVM Ensemble are evaluated on six datasets which are from UCI repository, Statlog or the famous research. The results of the experiment are compared with LibSVM, PCAenSVM and Bagging. PB-SVM Ensemble outperform other three algorithms in classification accuracy, and at the same time keep a higher confidence of accuracy than Bagging.

A MapReduce-based distributed SVM ensemble for scalable image classification and annotation

A combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them, support vector machine (SVM) ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive, especially when the number of replicated training datasets is large. This paper presents MRESVM, a MapReduce-based distributed SVM ensemble algorithm for scalable image annotation which re-samples the training dataset based on bootstrapping and trains an SVM on each dataset in parallel using a cluster of computers. A balanced sampling strategy for bootstrapping is introduced to increase the classification accuracy. MRESVM is evaluated in both experimental and simulation environments, and the results show that the MRESVM algorithm reduces the training time significantly while achieving a high level of accuracy in classifications.

More robust and better: a multiple kernel support vector machine ensemble approach for traffic incident detection

SUMMARYThis paper presents a multiple kernel support vector machine (MKL‐SVM) ensemble algorithm to detect traffic incidents. It uses resampling technology to generate training set, test set, and training subset firstly; then uses different training subsets to train individual MKL‐SVM classifiers; and finally introduces ensemble methods to construct MKL‐SVM ensemble to detect traffic incidents. Extensive experiments have been performed to evaluate the performances of the four algorithms: standard SVM, SVM ensemble, MKL‐SVM, and the proposed algorithm (MKL‐SVM ensemble). The experimental results show that the proposed algorithm has the best comprehensive performances in traffic incidents detection. To achieve better performances, the proposed algorithm needs less individual classifiers to construct the ensemble than SVM ensemble algorithm. Thus, compared with SVM ensemble algorithm, the complexity of the ensemble classifier of the proposed algorithm is reduced greatly. Conveniently, the proposed algorithm also avoids the burden of selecting the appropriate kernel function and parameters. Copyright © 2013 John Wiley & Sons, Ltd.

A Universal Steganalysis Algorithm for JPEG Image Based on Selective SVMs Ensemble

Universal steganalysis include feature extraction and steganalyzer design. Most universal steganalysis use Support Vector Machine (SVM) as steganalyzer. However, most SVM-based universal steganalysis are not to be very much effective at lower embedding rates. The reason why selective SVMs ensemble improve the generalization ability was analyzed, and an algorithm to select a part of individual SVMs according to their difference to build the ensemble classifier was proposed, which based on the selected ensemble theory-Many could be better than all. In this paper, the selective SVMs ensemble algorithm was used to construct a strong steganalyzer to improve the performance of steganographic detection. The twenty five experiments on the benchmark with 2000 different types of images show that: for popular steganography methods, and under different conditions of embedding rate, the average detection rate of proposed steganalysis method outperforms the maximum average detection rate for the steganalysis method based on single SVM with improving by 3.05%-12.05%; and for the steganalysis method based on BaggingSVM with improving by 0.2%-1.3%.

Traffic Incident Detection Using Multiple-Kernel Support Vector Machine

This paper presents applications of the multiple-kernel learning support vector machine (MKL-SVM) in traffic incident detection. The standard SVM was applied in traffic incident detection and achieved good results. However, the results depended greatly on the kernel function and parameters, and choosing the appropriate ones for the SVM was a procedure of trial and error. Unlike the SVM, the MKL-SVM used a convex combination of basic kernel functions instead of a single basic kernel function to construct an adaptive SVM model. This adaptive model could improve average performance in traffic incident detection just by randomly selecting the kernel function and parameters. As a result, the MKL-SVM avoided the burden of choosing the appropriate kernel function and parameters. The SVM ensemble algorithm trained many individual SVM classifiers to construct the classifier ensemble and then used this classifier ensemble to detect traffic incidents. Consequently, training occurred many times. Compared with the SVM ensemble algorithm, the training time cost of the MKL-SVM was much lower because training occurred only once. Extensive experiments were performed to evaluate the performance of three algorithms: standard SVM, SVM ensemble, and MKL-SVM. The experimental results showed that the performance of the MKL-SVM was much better than that of the standard SVM and slightly better than the SVM ensemble. More important, the performance of the MKL-SVM was stable.