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

Alham, Nasullah Khalid; Li, Maozhen; Liu, Yang; Qi, Man

doi:10.1016/j.camwa.2013.07.015

Cited by 41 publications

(13 citation statements)

References 35 publications

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“…Majority voting is proved to be an effective way of combining a number of weak classifiers into a strong classifier. Based on the voted result from each participated individuals, majority voting has a higher chance to achieve the correctly classified result.…”

Section: Mapreduce‐based Parallel Gepmentioning

confidence: 99%

MapReduce‐based parallel GEP algorithm for efficient function mining in big data applications

Liu

et al. 2017

Concurrency and Computation

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Summary Gene expression programming (GEP) algorithm is one of the most effective function mining algorithms in enabling the mathematical equation fitting for the input dataset. However, GEP algorithm encounters low efficiency issue in big data processing due to large overhead in its evolution when it handles the large‐scale data. In order to solve the issue, this paper presents two parallelized GEP algorithms using MapReduce. Based on data separation, the first algorithm aims at speeding up the large‐scale classification. However, it is lack of ability to output the mined equation explicitly. Therefore, based on the further improvements of the first algorithm, the second parallelized GEP algorithm aims at mining the equation efficiently and also outputs the equation explicitly and directly. The experimental results show that both algorithms are effective for processing large volume of data.

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Section: Mapreduce‐based Parallel Gepmentioning

confidence: 99%

MapReduce‐based parallel GEP algorithm for efficient function mining in big data applications

Liu

et al. 2017

Concurrency and Computation

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“…To be specific, MRSVM builds on the sequent minimal optimization algorithm for high efficiency in training and employs Map-reduce for parallel computation across a cluster of computers. Subsequently, they propose a Mapreduce-based distributed SVM ensemble algorithm (MRESVM) for scalable image annotation [55] that is designed based on the bagging architecture by training multiple SVMs on bootstrap training datasets and combining the output in an appropriate manner. In parti-cular, a balanced sampling strategy for bootstrap is introduced to increase the classification accuracy.…”

Section: Figure 4 Block Diagram Of Hmm-svm Approachmentioning

confidence: 99%

“…Classifiers Image Datasets Cusano, et al, [28] SVM WWW Dataset Li, et al, [30] Ensemble SVMs COREL/WWW Datasets Shi, et al, [31] SVM COREL/Other Datasets Goh, et al, [32] Ensemble SVMs COREL Dataset Tsai, et al, [34] Ensemble SVMs COREL Dataset Qi, et al, [35] SVM, MIL COREL Dataset Chapelle, et al, [37] SVM COREL14/COREL7 Datasets Wei, et al, [42] SVM COREL Dataset Tian, et al, [43] SVM-MK COREL Dataset Chen, et al, [45] DD-SVM COREL/MUSK Datasets Han and Qi [46] MIL, SVM COREL Dataset Yang, et al, [47] SVM, MIL COREL Dataset Zhao, et al, [48] TSVM, HMM COREL Dataset Lu, et al, [49] SVM COREL Dataset Jiang, et al, [50] SVM, LVQ COREL Dataset Gao, et al, [51] SVM, Semi-supervised EM COREL Dataset Huang, et al, [52] SVM, GMM, ACM COREL Dataset Lei, et al, [53] HMM-SVM COREL Dataset Alham, et al, [54] MRSVM, SMO, MapReduce COREL Dataset Alham, et al, [55] MRESVM, SMO, MapReduce COREL Dataset Qiu [56] SVM ImageCLEF2006 Dataset Serrano, et al, [59] Ensemble SVMs Other Dataset Feng, et al, [67] Ensemble SVMs COREL Dataset Boutell, et al, [68] SVM COREL/Other Datasets Fan, et al, [69] SVM COREL/WWW Datasets International Journal of Hybrid Information Technology Vol. 8, No.11 (2015)…”

Section: Sourcesmentioning

confidence: 99%

Support Vector Machine for Automatic Image Annotation

Tian¹

2015

IJHIT

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Automatic image annotation (AIA) is an active topic of research in computer vision and pattern recognition. In the last two decades, large amount of researches on AIA have been proposed, mainly including classification-based methods and probabilistic modeling methods. As one of the most common methods for AIA, support vector machine (SVM) has been widely applied in the multimedia research community, especially for image classification, image annotation and retrieval. However, compared with various SVM methods and their corresponding applications in the literature, there is almost no review research and analysis about SVM related studies. So the current paper, to start with, elaborates the basic principles of SVM. Followed by it summarizes SVM with applications to image annotation from three aspects of SVM ensemble for AIA, SVM with mixture of kernels for AIA and hybrid SVM for AIA respectively. In addition, SVM exploited in several other applications are also briefly reviewed. Finally, we end this paper with a summary of some important conclusions and highlight the potential research directions of SVM in automatic image annotation for the future.[11], regularized latent Dirichlet allocation (rLDA) [12] and TagProp [13], etc. By comparison, the former approach is relatively direct and natural to be understood. However, its performance is limited with the increase of the number of the semantic concepts and explosive multimedia data on the web. On the other hand, the latter often requires large-scale parameters to be estimated and the accuracy is strongly affected by the quantity and quality of the training data available. Furthermore, it has been argued that the discriminative approach can result in superior performance than that by generative method which is more powerful in capturing decision boundaries among different classes and more widely applied in recognition tasks. For this reason, it is highly desirable to develop approaches with the flexibility of generative learning and the performance of discriminative methods. At present, several hybrid generative discriminative schemes have been proposed to combine the strengths of these two kinds of models in a number of applications, from scene classification [14-16], object recognition [17,18], image annotation [19], image classification [20][21][22], speech recognition [23] to biological sequence analysis [24], resulting in state-of-the-art performance. Support vector machine (SVM) is a well-known example of discriminative methods. As a theoretically rich method and because of its advantages such as its use of over-fitting protect-ion independently from the number of features and its effectiveness in the case of sparse data, SVM has been extensively applied to a wide variety of domains such as computer vision, pattern recognition, object detection and function estimation, etc. In its basic form, an SVM creates a hyperplane as the decision plane, which separates the positive and negative classes with the largest margin. This paper aims to conduct a survey of SVM f...

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“…In this paper, the process of the traffic flow prediction algorithm based on GA-SVM in cloud computing environment is as follows [18].…”

Section: Parallel Genetic Svm Prediction Algorithmmentioning

confidence: 99%

Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

Yang

Mei

Yang

et al. 2014

Mathematical Problems in Engineering

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To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

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

Cited by 41 publications

References 35 publications

MapReduce‐based parallel GEP algorithm for efficient function mining in big data applications

MapReduce‐based parallel GEP algorithm for efficient function mining in big data applications

Support Vector Machine for Automatic Image Annotation

Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

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