Research on two-stage Feature Mining Fusion Method based on Genetic Algorithm

Abstract

In the actual fusion of two-stage feature data, reconstruction errors are easy to occur in the process of feature region reconstruction in different stages, resulting in too few final fusible mining indicators. To solve this problem, this paper designs a two-stage feature mining fusion method based on genetic algorithm. Based on the two-stage mining features, the two-stage feature areas are divided, and the feature area reconstruction project is controlled by setting the background template of the feature area. Then the genetic algorithm is used to construct the mining fusion mapping relationship, and different fusion levels are set to complete the research on the feature mining fusion method. Set up a two-stage feature experiment platform, mine feature data, take the feature data obtained as the experimental object, and use two traditional fusion methods to conduct comparative experiments with this article. The results show that the method in this paper can integrate the most index values, which proves that the method is more effective.