Prediction of the admission lines of college entrance examination based on machine learning

Accurate prediction to college entrance examination(CEE) results is very important for the candidates to fill in the application and the relevant analysis of the CEE. At present, the prediction of CEE scores is based on data statistics, probability model and some weighted combination models. Since g...

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Bibliographic Details
Published in2016 2nd IEEE International Conference on Computer and Communications (ICCC) pp. 332 - 335
Main Authors Zhenru Wang, Yijie Shi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
adaboost
admission line forecast
college entrance examination
Computational modeling
Data models
III-V semiconductor materials
Indium phosphide
machine learning
Predictive models
Online AccessGet full text
DOI10.1109/CompComm.2016.7924718

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Summary:Accurate prediction to college entrance examination(CEE) results is very important for the candidates to fill in the application and the relevant analysis of the CEE. At present, the prediction of CEE scores is based on data statistics, probability model and some weighted combination models. Since generating the model for predicting college admission lines uses too little reference factor, and the error is relatively large, so the reference value is very small. In this paper, machine learning methods are used to carry out the college admission lines of research and prediction. Specially, in this paper Adaboost algorithm is used to study and forecast, which belongs to ensemble learning. Finally, the result of this model is given, which is better than the current prediction method.
DOI:10.1109/CompComm.2016.7924718