Open AccessApplication of Weighted Classification of Non-equilibrium Decision Tree in Emotion Analysis of Poetry Reading
Author(s) -
Li Feng-Kun,
Yong Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012068
Subject(s) - interpretability , decision tree , id3 algorithm , computer science , robustness (evolution) , incremental decision tree , generalization , artificial intelligence , tree (set theory) , decision tree learning , machine learning , decision tree model , poetry , algorithm , data mining , mathematics , linguistics , mathematical analysis , biochemistry , chemistry , philosophy , gene
In this paper, a new unbalanced decision tree algorithm for appealing expressions of reading poem is proposed. This algorithm called Weighted Division of Unbalanced Decision Tree (WDOUDT). Compared to the traditional decision tree, it has fewer nodes and more interpretability, faster convergence and higher accuracy. WDOUDT is used to identify the moving expression when students read poem. The results show that WDOUDT performed better in accuracy than decision tree, and its time complexity is lower than that of the classical decision tree algorithm. Besides, it has good generalization ability and good robustness against noise data.