Fuzzy MLKNN in Credit User Portrait

Abstract

Aiming at the problems of subjective enhancement caused by the discretization of credit data and the lack of a multi-dimensional portrait of credit users in the current credit data research, this paper proposes an improved Fuzzy MLKNN multi-label learning algorithm based on MLKNN. On the one hand, the subjectivity of credit data after discretization is weakened by introducing intuitionistic fuzzy numbers. On the other hand, the algorithm is improved by using the corresponding fuzzy Euclidean distance to realize the multi-label portrait of credit users. The experimental results show that Fuzzy MLKNN performs significantly better than MLKNN on credit data and has the most significant improvement on One Error.