In credit card client classification problem, reducing misclassified rate is regarded as a key issue. Unfortunately, existing machine learning methods cannot be successfully applied to this problem. This paper introduces a classification model based on multiple criteria and multiple constraint levels linear programming (MC2LP), which equips two intervals of cutoff in the model. Two parallel hyperplanes are employed to indicate the relative positions between the points and hyperplanes. Then, we discuss the correctness of new model in error correction. Matrix representations of relevant models are also offered. Finally, compared to original MCLP, known MC2LP, Logistic Regression (LR) and Support Vector Machine (SVM), the propsed model shows superiority in solving two types of error related data mining problem.