# Fuzzy MLKNN in Credit User Portrait +
## 2 Literature Review
### Application of Fuzzy Theory  
Nowadays, fuzzy theory has also been used widely to measure uncertainty. It is a form of alternative mathematics suited for vagueness, especially for small quantities [32]. According to the characteristics of the multi-label problem, most of the multi-labels can be transformed into fuzzy numbers and then recognized and calculated through fuzzy rules using machine learning methods, such as clustering. The classic clustering algorithm is a fuzzy mean clustering algorithm. FCM is the most common way of introducing the membership function in fuzzy set theory into the calculation of distance to achieve better cluster division [33]. In recent years, scholars have not only improved fuzzy mean clustering on fuzzy computing rules [34; 35; 36] but also proposed a series of fuzzy clustering methods [37; 38; 39], such as Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) [40]. PIFHC considers intuitionistic fuzzy sets to deal with the uncertainty present in the data. Instead of using traditional hamming distance or Euclidean distance measure to find the distance between the data points, PIFHC uses the probabilistic Euclidean distance measure to propose a hierarchical clustering approach. And from experiments with the real-world car dataset  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Descriptions** & **Advantages** & **Disadvantages** \\ \hline Binary Correlation (BR) & Individual classifier for each label & Simple & Ignores label correlations \\ \hline Label Power Set (LP) & Each unique label set as a class identifier & Simple & Not applicable to more labels \\ \hline Chain of Classifiers (CC) & Extension of BR, String two classifiers into a chain for learning & Consider label correlations & Performance depends on the order of classifiers in the chain \\ \hline RankSVM & Improvement to the traditional SVM & Performance improvement & Not suitable for dealing with high-dimensional samples \\ \hline ML-DT & Improvement to the traditional DT & Performance improvement & Not suitable for processing continuous variables, large samples, and multi-class data \\ \hline MLKNN & Improvement to the traditional KNN & Strong applicability, Performance improvement & Ignores label correlations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of advantages and disadvantages of multi-label learning algorithms.  
and the Listeria monocytogenes dataset, intuitionistic distance can improve by 1-3.5% in the clustering accuracy.  
Based on the above research, MLKNN is a multi-label learning algorithm with excellent performance and strong applicability. At the same time, as an adaptive algorithm, it can process a large amount of data information quickly, and the operation efficiency is fast. Therefore, we select MLKNN as the learning algorithm, as the intuitionistic fuzzy number can express the discretized attribute variable value more objectively. Meanwhile, the distance calculation process existing in the MLKNN can be improved by a new fuzzy distance formula. We explore the intuitionistic fuzzy distance to improve the MLKNN algorithm, proposed as Fuzzy MLKNN.