改进型加权KNN算法的不平衡数据集分类

K最邻近(KNN)算法对不平衡数据集进行分类时分类判决总会倾向于多数类.为此,提出一种加权KNN算法GAK-KNN.定义新的权重分配模型,综合考虑类间分布不平衡及类内分布不均匀的不良影响,采用基于遗传算法的K-means算法对训练样本集进行聚类,按照权重分配模型计算各训练样本的权重,通过改进的KNN算法对测试样本进行分类.基于UCI数据集的大量实验结果表明,GAK-KNN算法的识别率和整体性能都优于传统KNN算法及其他改进算法.

Classification for Imbalanced Dataset of Improved Weighted KNN Algorithm

Based on analyzing the shortages of K-Nearest Neighbor（KNN） algorithm in solving classification problems on imbalanced dataset,a novel KNN approach based on weight strategy（GAK-KNN） is presented.The key of GAK-KNN lies on defining a new weight assignment model,which can fully take into account the adverse effects caused by the uneven distribution of training sample between classes and within classes.The specific steps are as follows： use K-means algorithm based on Genetic Algorithm（GA） to cluster the training sample set,compute the weight for each training sample in accordance to the clustering results and weight assignment model,use the improved KNN algorithm to classify the test samples.GAK-KNN algorithm can significantly improve the identification rate of the minority samples and overall classification performance.Theoretical analysis and comprehensive experimental results on the UCI dataset con？rm the claims.