Abstract: Credit evaluation is a key problem in finance,which predicts whether a user is at risk of defaulting and thus reduces bad debt losses.One of the key challenges in credit evaluation is the presence of a large number of invalid or redundant features in the dataset.To solve this problem,an improved feature selection using forest optimization algorithm(IFSFOA) is proposed.It addresses the shortcomings of the original algorithm FSFOA by using a cardinality check-based initialization strategy instead of randomized initialization in the initialization phase to improve the algorithm’s search capability;using a multi-level variation strategy in the local seeding phase to optimize the local search capability and solve the problems of restricted search space and localization of FSFOA;using a greedy selection strategy to select high-quality trees and eliminate low-quality trees when updating the candidate forest.In updating the candidate forest,we use the greedy selection strategy to select high-quality trees and eliminate low-quality trees,and converge the search dispersion process.Finally,the results show that IFSFOA outperforms FSFOA and more efficient feature selection algorithms proposed in recent years in terms of classification ability and dimension reduction ability,and validates the effectiveness of IFSFOA by setting up comparison experiments on public credit evaluation datasets covering low,medium and high dimensions.

Key words: Forest optimization algorithm, Feature selection, Credit evaluation, Evolutionary computation, Wrapper methods