# Fuzzy MLKNN in Credit User Portrait +
## 2 Literature Review  
### Multi-Label Learning  
Multi-label learning is an important branch of machine learning, which is different from traditional single-label learning in that there are multiple labels corresponding to each predicted sample [9]. In recent years, multi-label learning has been widely used in text classification [10; 11], sentiment analysis [12], image recognition, and other fields, and various multi-label classification algorithms have emerged one after another. The existing multi-label learning algorithms are mainly proposed from two perspectives: one is based on transformation, and the other is self-adaptation [13].  
From the transformation, it is always based on converting to binary classification, typical methods directly convert multi-label problems into multiple single-label problems, and each label is judged by the presence or absence [14; 15; 16]. The three most commonly used problem transformation methods are Binary Correlation (BR) [17], Label Power Set (LP) [18], and Chain of Classifiers (CC) [19]. BR transforms the multi-label problem into a set of independent binary problems. Then, each binary problem is processed using a traditional classifier. LP treats each unique label set as a class identifier, transforming the original multi-label dataset into a multi-class dataset. After using it to train a regular classifier, the predicted classes are inverse transformed to label subsets. However, with the number of tags increasing, the number of binary codes tends to increase exponentially, affecting the algorithm's performance, so it does not have good generalization ability. CC is an extension of BR, which strings two classifiers into a chain for learning, considering the correlation between labels, but the algorithm's performance needs to be improved. Both BR and LP are the basis of many multi-label ensemble-based methods. CC addresses the BR limitation by considering the label association task. [20; 21; 22].  
From the self-adaptation, it refers to the algorithms which can automatically adapt to the multi-label classification problem [23; 24; 25]. Typical adaptive multi-label classification algorithms are RankSVM, ML-DT, MLKNN, etc. RankSVM is an improvement to the traditional SVM, which modifies the loss in SVM to a ranking loss and optimizes the linear classifier to minimize the label ranking loss [26]. ML-DT is an improvement to the traditional decision tree. It draws on the idea of the decision tree to filter features according to the information gain to generate a classifier and uses the information gain to represent the feature's ability to discriminate all labels [27]. The MLKNN algorithm is a KNN-based multi-label classification algorithm [28; 29; 30]. The algorithm finds the K nearest neighbor samples. It performs statistical analysis on the labels of the K nearest neighbor samples to obtain the probability that the predicted sample contains each label. MLKNN outperforms some of the well-established multi-label learning algorithms mentioned above and is easy to understand and implement [31]. Furthermore, MLKNN is less restrictive in its use and is suitable for a wide variety of multi-label learning problems [28]. A summary of each algorithm, as well as the advantages and disadvantages, can be found in Table 1.