원문정보
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
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