Combining different classifiers in Educational Data Mining

被引：0

作者：

He Chuan ^{[1
]}

Li Ruifan ^{[1
]}

Zhong Yixin ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China

来源：

2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL V | 2010年

基金：

中国国家自然科学基金;

关键词：

data mining; logistic regression; k-nearest neighbor; singular value decomposition; classifiers combination;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Educational data mining is a crucial application of machine learning.The KDD Cup 2010 Challenge is a supervised learning problem on educational data from computer-aided tutoring. The task is to learn a model from students' historical behavior and then predict their future performance. This paper describes our solution to this problem. We use different classification algorithms, such as KNN, SVD and logistic regression for all the data to generate different results, and then combine these to obtainthe final result. It is shown that our resultsarecomparable to the top -ranked ones in leader board of KDD Cup 2010.

引用

页码：293 / 296

页数：4

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