SMOTE算法的介绍 在实际应用中，读者可能会碰到一种比较头疼的问题，那就是分类问题中类别型的因变量可能...
针对在数据样本不均衡时,K近邻(K-nearest Neighbor,KNN)方法的预测结果会偏向样本数占优类的问题,本文提出了一种基于合成少数类过采样方法(SMOTE)的KNN不均衡样本分类优化方法(KSID).该方法过程为:首先使用SMOTE方法将不均衡的训练集均衡化,并训练逻辑回归模型;然后使用逻辑回归模型对训练集进行预测,获取预测为正样本...
分类针对在数据样本不均衡时,K近邻(K-nearest Neighbor,KNN)方法的预测结果会偏向样本数占优类的问题,本文提出了一种基于合成少数类过采样方法(SMOTE)的KNN不均衡样本分类优化方法(KSID).该方法过程为:首先使用SMOTE方法将不均衡的训练集均衡化,并训练逻辑回归模型;然后使用逻辑回归模型对训练集进行预测,获取预测为...
Consequently, this paper introduces a novel method named KNN-SMOTE, an enhancement of the Borderline-SMOTE technique. Empirical assessments conducted on five imbalanced benchmark datasets from the UCI Machine Learning Repository underscore the superior performance of our approach in terms of F-score, G...
In SMOTE-LMKNN, the local mean-based KNN (LMKNN) is first introduced to describe the local characteristic of imbalanced data. Second, a new LMKNN-based noise filter is proposed to remove noise and unsafe borderline samples. Third, the interpolation between a base sample and its LMKNN is ...
kNN AlgorithmSCADA datasetSC-SMOTE AlgorithmBecause SCADA monitoring data of wind turbines are large and fast changing, the unbalanced proportion of data in various working conditions makes it difficult to process fault feature data. The existing methods mainly introduce new and non-repe...
At the same time, the SMOTE-Tomek Links address the imbalanced class. These proposed approaches to handle both issues are then used to assess the air quality prediction of the India AQI dataset using Naive Bayes (NB), KNN, and C4.5. The five treatments show that the proposed...
The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97% accuracy for detectingCKD. This in-depth analysis demonstrates the model's capabilities and underscores its potential as a valuable tool in the diagnosis of CKD....
Simple Summary: This paper presents a cervical cancer detection approach where the KNN Imputer techniques is used to fill the missing values and after that SMOTE upsampled features are utilized to train a multi-model ensemble learning approach. Results demonstrate that use of KNN Imputed SMOTE ...