关键词: 不平衡数据, 核主成分分析, 支持向量机, 偏移

Abstract: A balance method for the offset of separation hyperplane of biclassification imbalanced data is proposed.Firstly，the principal eigenvalues are found respectively of the two classes of samples in feature space by using Kernel Principal Component Analysis（KPCA）.Secondly，one penalty proportion is given based on the information provided by the sizes of the two sample data and their eigenvalues.Finally，a new separation hyperplane is generated by the optimization training.The hyperplane revises the error of the standard Support Vector Machines.Experiments show the efficiency of proposed method，i.e.comparing with standard Support Vector Machines the classification error can be balanced and be also decreased.

Key words: imbalanced data, Kernel Principal Component Analysis（KPCA）, Support Vector Machines（SVM）, offset