MapReduce-Based Bayesian Automatic Text Classifier Used in Digital Library

Bayesian theorem is an effective method for text classification. But it will consume too much time and resource when used in large-scale database such as digital library. How to process data efficiently becomes a vital problem for the further development of digital library. Because Mapreduce model h...

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Bibliographic Details
Published inComputational Intelligence and Intelligent Systems pp. 121 - 126
Main Authors Niu, Zhen, Yin, Zelong, Cui, Huayang
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesCommunications in Computer and Information Science
Subjects
Bayesian Text Classifier
Cloud Computing
Digital Library
Machine Learning
Mapreduce
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Summary:Bayesian theorem is an effective method for text classification. But it will consume too much time and resource when used in large-scale database such as digital library. How to process data efficiently becomes a vital problem for the further development of digital library. Because Mapreduce model has strong capacity of processing mass data, Mapreduce-based Bayesian classifier can reduce the time that is caused by machine learning and therefore enhance the overall efficiency. The new method can classify documents into different groups according to its subject. In this way, it is helpful for management and storage of information. Thus Mapreduce-based Bayesian text classifier can be used in digital library successfully which will provide better service for people.
ISBN:3642342884
9783642342882
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-642-34289-9_14