# Fuzzy MLKNN in Credit User Portrait +
## 4 Experiments
### Comparison with Fuzzy MLKNN and Other Multi-Label Learning Algorithms  
After determining the most suitable K value, this paper further compares the performance of the improved algorithm proposed with other commonly used multi-label learning algorithms on the credit data set. According to the classification of multi-label learning algorithms, this paper considers two types of multi-label learning algorithms. One is the multi-label algorithm based on problem transformation, including Binary Relevance and Classifier Chain, and the other is the adaptive algorithm Rank SVM. As a result, the performance of the algorithm is studied. The comparison results are shown in Table 8.  
By comparing the five indicators in the table, it is found that the improved Fuzzy MLKNN shows better and better performance in learning the credit data set. Among them, the two indicators of _HammingLoss_ and _OneError_ have the most obvious advantages. This paper finds that its learning time is relatively short. In addition, comparing the five indicators of other algorithms, it is found that the performance of Rank SVM is second, the performance of the _RankingLoss_ indicator is relatively good, and the performance of Classifier Chain on the credit data set is the worst. Therefore, this paper further uses the improved MLKNN to predict the test set data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Coding** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{3}{*}{Labels Name} & Personal & Personal & Low & Medium & Low & Normal & High \\  & development & development & frequency of & frequency of & & attention to & attention to \\  & stability & instability & credit activities & credit activities & credit status & credit status & credit status & credit status \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Label information conversion process record. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Binary Relevance** & **Classifier Chain** & **Rank SVM** & **Fuzzy MLKNN** \\ \hline _HammingLoss_ & 0.1947 & 0.2584 & 0.1688 & 0.0867 \\ \hline _Average\_Precision_ & 0.8652 & 0.7542 & 0.8944 & 0.9436 \\ \hline _RankingLoss_ & 0.1281 & 0.2410 & 0.0900 & 0.0500 \\ \hline _OneError_ & 0.0852 & 0.2130 & 0.0667 & 0.0133 \\ \hline _Coverage_ & 3.0500 & 3.5200 & 2.9900 & 2.5267 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison between different multi-label algorithms. Figure 4: _One Error_ under different K values. Figure 5: _Coverage_ under different K values. Figure 3: _RankingLoss_ under different K values. The smaller the _HammingLoss_, the smaller the average number of misclassifications, and therefore the better the performance. From Figure 1, it can be directly seen that the _HammingLoss_ value of the improved algorithm has an overall improvement compared with the traditional algorithm. The _HammingLoss_ value of the traditional algorithm is between 0.12 and 0.15, and the mean value is 0.132. The overall fluctuation is relatively stable, and the distribution of the smallest _HammingLoss_ value is not obvious, which is obtained at K = 14, 28, 31, and 34, respectively. The _HammingLoss_ value of the improved algorithm is between 0.08 and 0.11, with an average value of 0.096. During the change from K = 10 to 35, the overall trend decreases. The smallest _HammingLoss_ value can be easily obtained from the figure at K = 35. At the same time, we can find that the algorithm's performance before and after the improvement in _HammingLoss_ is not synchronous with the variation of the K value. From the perspective of _HammingLoss_ only, the value of K selected for the optimal performance of the traditional algorithm and the improved algorithm is different. The higher the _Average_Precision_, the better the performance of the multi-label learning algorithm.