Author:
Sawangarreerak Siriporn,Thanathamathee Putthiporn
Abstract
In this work, we propose a combined sampling technique to improve the performance of imbalanced classification of university student depression data. In experimental results, we found that combined random oversampling with the Tomek links under sampling methods allowed generating a relatively balanced depression dataset without losing significant information. In this case, the random oversampling technique was used for sampling the minority class to balance the number of samples between the datasets. Then, the Tomek links technique was used for undersampling the samples by removing the depression data considered less relevant and noisy. The relatively balanced dataset was classified by random forest. The results show that the overall accuracy in the prediction of adolescent depression data was 94.17%, outperforming the individual sampling technique. Moreover, our proposed method was tested with another dataset for its external validity. This dataset’s predictive accuracy was found to be 93.33%.
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献