Improved K-nearest neighbor algorithm and its application in learning and warning

doi:10.3969/j.issn.1000-1565.2020.02.012

Abstract

Abstract: With the increasing role of big data in education,a large amount of data is applied to teaching research,teaching evaluation and behavior prediction.Education data,such as students grades,behavioral records,and interaction with teachers,have begun to show their value.In order to solve the problem of low pass rate in the course,improved K-nearest neighbor algorithm is applied to study the early warning.The grid search and cross validation method of combining the parameter optimization of the model was used first.Second in the process of constructing a decision tree,the Gini gain is used to determine the characteristics of the weight coefficient and according to the weight coefficient of feature selection,weight coefficient was introduced when calculating the distance,enables each feature received weight coefficient constraint.Experiments show that the improved K-nearest neighbor algorithm is significantly better than the traditional K-NN algorithm in both a public data set and a real data set.

Key words: educational data mining, grid search, K-nearest neighbor, cross validation, Gini gain

CLC Number:

TP399

ZONG Xiaoping,TAO Zeze. Improved K-nearest neighbor algorithm and its application in learning and warning[J]. Journal of Hebei University (Natural Science Edition), 2020, 40(2): 193-199.

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