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Computer Science Paper Classification for CSAR

  • Jiahui QuanEmail author
  • Qing Li
  • Minglu Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8699)

Abstract

When researchers or students entering a new research field in computer science, they desire to know who the top scientists are and what the best papers are in this field, then they know to find whom to collaborate with or can find best papers in this area to read. In order to divide different research fields, it is very important to correctly classify all the papers in computer science. In this paper, we propose CSAR classification system derived from 2012 ACM Computing Classification System (CCS), and also propose a new weighted naive Bayes classifier to classify the papers in top publications by their research fields. The experiments show that the performance of proposed weighted naive Bayes classifier is better than the unweighted naive Bayes classifier and overwhelms the results of \(k\)-NN classifier.

Keywords

Text classification Weighted naive Bayes classifier CSAR classification system Academic information platform 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.City University of Hong KongHong KongPeople’s Republic of China

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