The improvement of Smote algorithm

What is it about?

Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote > K-RBFNN Smote > FCM K-RBFNN Smote > RUS K-RBFNN Smote > K-Means Smote > FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling.

Featured Image

Photo by Sven Mieke on Unsplash

Photo by Sven Mieke on Unsplash

Why is it important?

Our discoveries adjust the resampling techniques by adding the FCM and linear interpolation link, and they combine the RBFNN and K-means clustering methodology to enhance Smote by altering the interpolation method from the algorithm dimension.In the area of senior well-being, the improved model outperforms the same dataset in terms of accuracy, AUC, Kappa, and other measures.

Perspectives