TY - GEN
T1 - Computer science paper classification for csar
AU - Quan, Jiahui
AU - Li, Qing
AU - Li, Minglu
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Academic information platform
KW - CSAR classification system
KW - Text classification
KW - Weighted naive Bayes classifier
UR - https://www.scopus.com/pages/publications/84917690647
UR - https://www.scopus.com/pages/publications/84917690647#tab=impact
U2 - 10.1007/978-3-319-13296-9_4
DO - 10.1007/978-3-319-13296-9_4
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783319132952
VL - 8699
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 34
EP - 43
BT - New Horizons in Web Based Learning
A2 - Väljataga, Terje
A2 - Tang, Jeff K.T.
A2 - Cao, Yiwei
A2 - Laanpere, Mart
A2 - Leung, Howard
PB - Springer Verlag
T2 - 7th International Workshop on Social and Personal Computing for Web-Supported Learning Communities, SPeL 2014, 1st International Workshop on Peer-Review, Peer-Assessment, and Self-Assessment in Education, PRASAE 2014, 2014 International Workshop on Mobile and Personalized Learning, IWMPL 2014, 1st International Workshop on Open Badges in Education, OBIE 2014, 4th International Symposium on Knowledge Management & E-Learning, KMEL 2014 and Future of e-Textbooks Workshop, FeT 2014 in held with 13th International Conference on Web-based Learning, ICWL 2014
Y2 - 14 August 2014 through 17 August 2014
ER -