K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes

H. Hairani, Khurniawan Eko Saputro, Sofiansyah Fadli
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引用次数: 17

Abstract

The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.
利用 K-means-SMOTE 处理 C4.5、SVM 和天真贝叶斯糖尿病分类中的类不平衡问题
数据集中出现不平衡类会导致分类结果倾向于数据量最大的类(多数类)。这就需要一种抽样方法来平衡少数类(正类),使类的分布趋于平衡,从而获得更好的分类结果。本研究使用 k-means-SMOTE 克服了印度皮马糖尿病疾病数据集的不平衡类问题。该数据集有 268 个正类实例(少数类)和 500 个负类实例(多数类)。在数据采样中使用 k-means-SMOTE 时,通过比较 C4.5、SVM 和 naïve Bayes 进行了分类。使用 k-means-SMOTE 时,SVM 分类方法的准确率和灵敏度最高,分别为 82 % 和 77 %,而天真贝叶斯方法的特异性最高,为 89 %。
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