Classification Method of Teaching Resources Based on Improved KNN Algorithm

Authors

  • Yingbo An Applied Technology Research and Development Center of Intelligent Finance in Hebei Universities, Hebei Finance University, Hebei Baoding
  • Meiling Xu
  • Chen Shen Applied Technology Research and Development Center of Intelligent Finance in Hebei Universities, Hebei Finance University, Hebei Baoding

DOI:

https://doi.org/10.3991/ijet.v14i04.10131

Keywords:

text classification, KNN, primary and secondary school teaching resources, sample cutting

Abstract


In order to effectively utilize the network teaching resources, a teaching resource classification method based on the improved KNN (K-Nearest Neighbor) algorithm was proposed. Taking the text class primary and secondary school teaching resources as the research object, combined with the domain characteristics, the KNN algorithm was improved. By measuring the sample space density, the text of the high-density area was found. Different clipping methods were proposed for both intra-class and inter-class regions. The problem of cropping in the space of multiple class boundaries was considered. Results showed that the method ensured uniform distribution of samples and reduced the time of classification. Therefore, under the Weka platform, the improved KNN algorithm is effective.

Author Biography

Meiling Xu

Applied Technology Research and Development Center of Intelligent Finance in Hebei Universities, Hebei Finance University, Hebei Baoding

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Published

2019-02-27

How to Cite

An, Y., Xu, M., & Shen, C. (2019). Classification Method of Teaching Resources Based on Improved KNN Algorithm. International Journal of Emerging Technologies in Learning (iJET), 14(04), pp. 73–88. https://doi.org/10.3991/ijet.v14i04.10131

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Papers