Customer value segmentation based on cost-sensitive learning Support Vector Machine

Two types of errors, 'rejecting true and accepting false' are inevitable in customer value segmentation. The traditional data mining method which is on the total accuracy rate cannot reflect the influence caused by the great difference of misclassification costs and unbalanced quantity distribution of customers who have various values. The thesis proposes two enhancements. The first is developing the cost-sensitive Support Vector Machine (SVM) classifier by presenting misclassification cost function based on customer value, which is evaluated by the function of the exceptional lost. The second enhancement is a method of multi-category classification based on the binary classification version of SVM. The data test result proves that the method can control the different types of errors distribution with various cost of misclassification accurately, reduce the total misclassification cost and distinguish the customer value effectively.