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This research aimed at the case of customers default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.
Data Set Characteristics: Multivariate Number of Instances: 30000 Area: Business Attribute Characteristics: Integer, Real Number of Attributes: 24 Date Donated 2016-01-26 Associated Tasks: Classification Missing Values? N/A Number of Web Hits: 527861
Attribute Information: This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables: X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. X2: Gender (1 = male; 2 = female). X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). X4: Marital status (1 = married; 2 = single; 3 = others). X5: Age (year). X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above. X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.
Learning Vector Quantization is used for the classification of the dataset. At first two different vector is chosen for creating the weight matrix then with train data, weight matrix is getting updated. Learning rate is getting reduced every iteration. When all the iterations are done we have two vectors (weight matrix). The test data gets classified by using weight matrix.
Recall (First Class) 0,754 Precision (First Class) 0,999 F-Score (First Class) 0,859 Recall (Second Class) 1,000 Precision (Second Class) 0,933 F-Score (Second Class) 0,965 Macro Recall 0,736 Micro Recall 0,730 Macro F-Score 0,847 Micro F-Score 0,843
-First Class: 1 (Default Payment: Yes) -Second Class: 0 (Default Payment: No)
•Dataset Name: I-Cheng Yeh email addresses: (1) icyeh '@' chu.edu.tw (2) 140910 '@' mail.tku.edu.tw institutions: (1) Department of Information Management, Chung Hua University, Taiwan. (2) Department of Civil Engineering, Tamkang University, Taiwan. other contact information: 886-2-26215656 ext. 3181
•http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients (dataset link)