Earticle

초록

영어

Support Vector Machine (SVM) is extremely powerful and widely accepted classifier in the field of machine learning due to its better generalization capability. However, SVM is not suiTable for large scale dataset due to its high computational complexity. The computation and storage requirement increases tremendously for large dataset. In this paper, we have proposed a MapReduce based SVM for large scale data. MapReduce is a distributed programming model which works on large scale dataset by dividing the huge datasets in smaller chunks. MapReduce distribution model works on several frame works like Hadoop Twister and so on. In this paper, we have analyzed the impact of penalty and kernel parameters on the performance of parallel SVM. The experimental result shows that the number of support vectors and predictive accuracy of SVM is affected by the choice of these parameters. From experimental results, it is also analyzed that the computation time taken by the SVM with multi-node cluster is less as compared to the single node cluster for large dataset.

목차

Abstract
 1. Introduction
 2. Support Vector Machine
 3. Parallel SVM
 4. Hadoop Framework
  4.1. The Hadoop Distributed File System (HDFS)
  4.2. MapReduce Programming Model
 5. MapReduce Based Parallel SVM
 6. Experimental Setup
  6.1. Sequential SVMVs. Parallel SVM
  6.2. Experiments to Analyze the Effects of Penalty Parameters C and Gaussian Kernel Parameter and Number of Nodes on Number of Support Vectors and its Accuracy on different Datasets.
 7. Conclusions and Future Work
 References

키워드

저자

• Anushree Priyadarshini [ Indian Institute of Information Technology, Allahabad, India ]
• SonaliAgarwal [ Indian Institute of Information Technology, Allahabad, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605