Decision tree based prediction system for word difficulty classification

HIGHLIGHTS

• What: The aim of this study was to use decision tree modelling to predict the general proficiency of the public in words with different attributes. In this study, the clustering error of all sample data was evaluated using the sum of squared errors (SSE), which represents the overall clustering effectiveness. Through the fusion of the decision tree model and the K-Means clustering algorithm, this study have developed an objective English word difficulty classification system.
• Who: Personalized, Annotations. and Xinyi, Jiang from the Liaoning University, No, Chongshan Middle Road, Huanggu District, Shenyang, Liaoning Province, China . . .
  

   

  

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