Earticle

초록

영어

With the large-scale application of high dimensional gene expression data which exists lots of redundant information, it may waste a lot of time in feature selection and classification. By analyzing the process of MapReduce computing paradigms on cloud platform, it is found that the feature selection which through parallel and distributed computing in MapReduce combined with extreme learning machine is appropriate for constructing a recognition method. This paper proposed a MapReduce algorithm on high gene feature for parallel and distributed selection and classification, aiming to save time resources to make a higher accuracy in training process on large scale gene datasets. Simulation experiments on gene datasets show that the running time on cloud platform is greatly shortened by the time promising the high classification accuracy.

목차

Abstract
 1. Introduction
 2. Gene Filters Based on Information Gain
  2.1. Information Entropy and Information Gain
  2.2. Information Gain Process
 3. Classification Model Built Based on Cloud Computing Platform
  3.1. MapReduce-based Feature Selection Model
  3.2. MapReduce-based Gene Expression Data Classification Model
 4. Experiment
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Wei Sha-Sha [ College of Information Engineering, China JiLiang University ]
• Lu Hui-Juan [ College of Information Engineering, China JiLiang University ] Corresponding author
• Jin Wei [ College of Information Engineering, China JiLiang University ]
• Li Chao [ College of Information Engineering, China JiLiang University ]

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