# Fuzzy MLKNN in Credit User Portrait +
## 4 Experiments
### Comparison with Fuzzy MLKNN and Other Multi-Label Learning Algorithms
It can be observed from Figure 2 that the _Average_Precision_ of the improved algorithm is significantly better than the traditional algorithm, and the gap between the two becomes more obvious with the increase of the K value. The value of _Average_Precision_ of the traditional algorithm is between 0.89 and 0.92, with an average value of 0.909, and as the value of K increases, the overall trend decreases. The algorithm performs best when K = 14. The value of _Average_Precision_ of the improved algorithm is between 0.92 and 0.95, with a mean value of 0.938, and with the increase of the K value, the overall trend is upward. When the value of K takes a value near 36, the performance is optimal, and when K = 36, the value of Average_Precision is at most 0.944. Since _Rankingloss_ and _OneError_ are considered indicators based on the order of labels and are in the same order of magnitude, the smaller the two indicators are, the better the performance is. This paper considers these two indicators at the same time. In Figure 3, the blue line is the _RankingLoss_ with MLKNN, and in Figure 4, the green columnar presents _OneError_ values with MLKNN. Respectively, the red line in Figure 3 and the orange columnar in Figure 4 are the _RankingLoss_ and _OneError_ values of Fuzzy MLKNN. First of all, from the value of _RankingLoss_, the improved algorithm is better than the traditional algorithm, and as the value of k increases, this advantage is gradually obvious. The average value of the traditional algorithm on _RankingLoss_ is 0.08, and the low values are obtained at K = 8, 11, and 14. The mean _RankingLoss_ of the improved algorithm is 0.055, and the minimum value is obtained at K= 36. Secondly, from the perspective of the _OneError_ value, it can be seen from the figure that the _OneError_ value of the traditional algorithm is significantly higher than that of the improved algorithm, and the former value fluctuates greatly. The average _OneError_ value of the traditional algorithm is 0.063, while the average _OneError_ value of the improved algorithm is 0.016. Based on the consideration of the _OneError_ value, the optimal K value of the traditional algorithm is obtained when K = 14; the optimal K value of the improved algorithm is obtained when K = 17. Figure 5 shows the performance of _Coverage_ under different K values. _Coverage_ is the step size required to traverse the correct label. The smaller the index, the better the performance. From the figure, we can see that the _Coverage_ of the improved algorithm is also significantly lower than that of the traditional algorithm. The mean value of the improved algorithm on _Coverage_ is 2.585, and the traditional algorithm is 2.822. The minimum _Coverage_ values of the improved algorithm can be obtained at 24-26, 36, while the minimum _Coverage_ values of the traditional algorithm are obtained at 10, 11, and 14. Through the analysis of the above experimental results, we find that the improved algorithm is better than the traditional algorithm in all evaluation metrics, and the performance of the _OneError_ value is the most significant, proving that the fuzzy distance measure is effective in the multi-label learning process of credit data. In addition, through the analysis of the optimal selection of the K value on different indicators, this paper finds that the traditional algorithm and the improved algorithm are not consistent in the selection of the optimal K value. The selection of the optimal K value of the traditional algorithm is significantly lower than the improved algorithm. From the comprehensive consideration of the performance of the above five indicators, it is more appropriate to take 14 for the optimal K value of the traditional algorithm. And the optimal K value of the improved algorithm is 36. Therefore, for these two results, we mainly use the improved algorithm with better performance in the following analysis process and select the K value of 36 to conduct further in-depth research on the data.