[1]YANG Zhengli,SHI Wen,CHEN Haixia,et al.The strategy of college enrollment predicted with big data[J].CAAI Transactions on Intelligent Systems,2019,14(2):323-329.[doi:10.11992/tis.201709011]
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The strategy of college enrollment predicted with big data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 2 Page number: 323-329 Column: 学术论文—机器学习 Public date: 2019-03-05
Title:
The strategy of college enrollment predicted with big data
Author(s):
YANG Zhengli;  SHI Wen;  CHEN Haixia;  WANG Changpeng
School of mechanical and electrical engineering, SanJiang University, Nanjing 210012, China
Keywords:
big data; machine learning; deep learning; learning algorithm; college enrollment; strategy prediction; random forest; cloud computing
CLC:
TP311
DOI:
10.11992/tis.201709011
Abstract:
Considering the decline in the enrollment of high school students and the expansion in the scale of enrollment of colleges and universities, methods of enrollment are developing continuously, and the competition among colleges and universities is becoming fierce. Under this background, an important issue that colleges and universities need to consider is to accurately locate the source of students by using the tremendous amount of heterogeneous enrollment data and accomplish the pre-enrollment propagation. Combined with the cloud computing technology, the parallel computing model MapReduce and the memory parallel computing framework Spark are used to analyze historical enrollment data. The paralleled random forest algorithm is proposed to predict the strategy of college enrollment. This model has a shorter prediction time, improved prediction accuracy, and improved big data processing ability. The experimental result shows that the performance of the paralleled random forest algorithm in different datasets is significantly superior to the widely used decision tree prediction method.
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