特徵選取演算法對可讀性模型的影響

Reading is one of the most important ways of acquiring knowledge. Researchers have pointed out that to promote the effectiveness of reading, it is very important to provide materials of the right level of difficulty. If the reading materials are too easy, readers usually cannot acquire new knowledge in the process of reading; on the other hand, if the materials are too difficult, it will cause excessive cognitive burden to the readers, affecting their learning effectiveness. Therefore, giving readers appropriate reading is an important issue. To address this issue, many scholars have begun to develop readability models and found that feature selection enhances the accuracy of readability models. However, the interaction between various feature algorithms and classifiers has yet to be much explored in past studies. Therefore, in this study, three feature selection algorithms, Chi-squared test, ANOVA, Mutual Information, and 25 classifiers, were applied to compare the accuracy of readability models for grades 1-12 in the textbooks of the Chinese language. The experimental results show the feature selection algorithm and the paired classifiers with the highest accuracy. This study found that using ANOVA as the feature selection algorithm and LGBM as the classifier can have 48% accuracy, 73% adjacent accuracy, and 85% reduction in the number of features.