关键词: 不均衡数据集, K最近邻居（KNN）算法, 过抽样, 交叉算子

Abstract: When the KNN algorithm is used to deal with imbalanced data sets，it has poor performance in the minority class prediction accuracy.An improved algorithm（G-KNN） is proposed to solve this problem.For the minority class samples，this algorithm uses the crossover operator and mutation operator to generate some of the new minority class samples.One new sample is considered valid，only if its Euclidean distance to parent is less than the maximum distance between parents.Then this valid sample is used to product the minority class samples in the next round of the process.The experimental results，which are tested on the UCI data sets，show that this algorithm is superior to KNN algorithm in the application of random over-sampling in improving the classification accuracy of the minority class.

Key words: imbalanced data sets, K-Nearest Neighbor（KNN） algorithm, over-sampling, crossover