Handling Data Imbalance Problem in Hybrid Resampling Approach to Improve Accuracy of K-Nearest Neighbors Algorithm
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Abstract
Handling the problem of data imbalance is a crucial challenge in the development of classification models, especially in medical data such as stroke detection. This study proposes a hybrid resampling approach of SMOTE (Synthetic Minority Over-sampling Technique) and NearMiss to improve the accuracy of K-Nearest Neighbors (KNN) algorithm on stroke datasets. Our hybrid resampling approach aims to overcome the shortcomings of each resampling technique, with SMOTE generating minority class samples and NearMiss subtracting samples from the majority class. We test this approach on a stroke dataset that has class imbalance. The method was evaluated using K-NN. The experimental results show that the hybrid approach can improve the accuracy of K-NN in predicting the minority class compared to the conventional approach. It shows that adjusting these parameters can significantly affect the performance of the hybrid approach. In this study, providing the highest accuracy in SMOTE with K-1 neighbors resulted in a 100% improvement in accuracy, followed by a 97% improvement with K-2, and a 93% accuracy with K-3. On the other hand, the undersampling approach using NearMiss showed 100% accuracy improvement with K-1, followed by 74% improvement with K-2, and 76% accuracy with K-3. In conclusion, the use of SMOTE proved to be more consistent in improving accuracy with higher K values. In this case, it is important to consider various parameters in choosing the right resampling technique to handle data imbalance.
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