# Fuzzy MLKNN in Credit User Portrait +
## 4 Experiments
### Experiment Setting
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \multicolumn{1}{c}{**Coefficient of Correlation**} & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** & **(8)** & **(9)** & **(10)** \\ \hline (1) year\_income & 1 & 0 & 0 & 0 & 0.01 & 0.01 & 0 & 0 & 0.04 & 0.03 \\ (2) credit\_over\_amount & 0 & 1 & 0.02 & 0.03 & \(-\)0.02 & \(-\)0.03 & \(-\)0.02 & \(-\)0.02 & \(-\)0.01 & \(-\)0.02 \\ (3) loan\_over\_amount & 0 & 0.02 & 1 & 1 & \(-\)0.01 & \(-\)0.01 & \(-\)0.02 & \(-\)0.02 & 0 & \(-\)0.01 \\ (4) total\_over\_amount & 0 & 0.03 & 1 & 1 & \(-\)0.01 & \(-\)0.01 & \(-\)0.02 & \(-\)0.02 & 0 & \(-\)0.01 \\ (5) bank\_legal\_org\_num & 0.01 & \(-\)0.02 & \(-\)0.01 & \(-\)0.01 & 1 & 0.99 & 0.94 & 0.94 & 0.58 & 0.64 \\ (6) bank\_org\_num & 0.01 & \(-\)0.03 & \(-\)0.01 & \(-\)0.01 & 0.99 & 1 & 0.93 & 0.93 & 0.59 & 0.65 \\ (7) credit\_legal\_org\_num & 0 & \(-\)0.02 & \(-\)0.02 & \(-\)0.02 & 0.94 & 0.93 & 1 & 1 & 0.58 & 0.63 \\ (8) credit\_org\_num & 0 & \(-\)0.02 & \(-\)0.02 & \(-\)0.02 & 0.94 & 0.93 & 1 & 1 & 0.58 & 0.63 \\ (9) total\_credit\_amount & 0.04 & \(-\)0.01 & 0 & 0 & 0.58 & 0.59 & 0.58 & 0.58 & 1 & 0.52 \\ (10) query & 0.03 & \(-\)0.02 & \(-\)0.01 & \(-\)0.01 & 0.64 & 0.65 & 0.63 & 0.63 & 0.52 & 1 \\ \hline \end{tabular}
\end{table} 
Table 3: Correlation matrices of some variables. the non-membership degree is 1 minus the membership degree. The specific processing processes are shown in Tables 5 and 6. After the attributes are processed, the label information must be converted into numerical values. The label information is obtained from the experience data of financial institutions. The specific label information and serial numbers are shown in Table 7. In \begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute Name** & **Corresponding Intuitionistic Fuzzy Number** \\ \hline education & 1:(0.01, 0.99); 2:(0.10, 0.90); 3:(0.17, 0.83); 4:(0.41, 0.59); \\  & 5:(0.79, 0.21); 6:(0.98, 0.02); 7(1, 0) \\ \hline year\_income & 1:(0.01, 0.99); 2:(0.40, 0.60); 3:(0.73, 0.27); 4:(0.95, 0.05); \\  & 5:(0.98, 0.02); 6:(1, 0) \\ \hline credit\_amount\_utilization\_rate & 1:(0.09, 0.91); 2:(0.20, 0.80); 3:(0.58, 0.42); 4:(0.97, 0.03); 5:(1, 0) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fuzzification process. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute Name** & **Data Conversion Process** \\ \hline \multirow{2}{*}{education} & Primary school = 1; Secondary technical school = 2; Junior high school = 3; Senior middle school =4; Junior college = 5; \\  & University = 6; Postgraduate = 7 \\ \hline \multirow{2}{*}{year\_income} & 1-10,000 RMB = 1; 10,001–50,000 RMB = 2; 50,001–100,000 RMB = 3; \\  & 100,001–500,000 RMB = 4; 500,001–1,000,000 RMB = 5; more \\  & than 1,000,000 RMB = 6 \\ \hline \multirow{4}{*}{career} & Soldier = 1; Heads of state agencies, party organizations, enterprises, and institutions = 2; Clerks and related personnel = 3; \\  & Production personnel in agriculture, forestry, animal husbandry, fishery, and water conservancy = 4; Commercial and service \\  & industry personnel = 5; Professional skill worker = 6; \\  & Production and transportation equipment operators and related \\  & personnel = 7 \\ \hline credit\_account & 1–5 = 1; 6\(\sim\)10 = 2; 11–20 = 3; 21–50 = 4; more than 50 = 5; \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Representative the credit variable data conversion process record.