Classification for Imbalanced Dataset of Improved Weighted KNN Algorithm
Tao Zhang · Jisuanji gongcheng · 2012
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.