[Retracted] Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity

Abstract

The swarm intelligence algorithm simulates the behavior of animal populations in nature and is a new type of intelligent solution that is different from traditional artificial intelligence. Feature selection is a very common data dimensionality reduction method, which requires us to select the feature subset with the best evaluation criteria from the original feature set. Feature selection, as an effective data processing method, has become a hot research topic in the fields of machine learning, pattern recognition, and data mining and has received extensive attention and attention. In order to verify the improvement effect of the feature selection algorithm based on the swarm intelligence algorithm on the data, this paper conducts experiments on six classes in the city’s first middle school with similar conditions. First, count the current situation of the students in the class, then divide them into classes, use different algorithms to teach them, and count the changes of the students after a period of teaching. The experiment found that the performance of students under the feature selection algorithm is about 30% higher than other teaching methods, and the awareness of cooperation between students reaches 0.8. It solves the contradiction between popularization and improvement and solves the problems of polarization and transformation of underachievers. The individuality of the algorithm has been fully utilized and developed. The test results show that the improved algorithm has faster convergence speed and higher solution accuracy, and the feature selection algorithm based on swarm intelligence algorithm can effectively improve the efficiency of the algorithm.

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“…To verify the data enhancement results of the characteristic choice alcohol system, which is based on the group smart algorithm, Chen et al conducted experiments in six classes with similar conditions in the first middle school of the city. e experiments showed that, under the feature selection algorithm, students' academic performance was approximately 30% greater than the other methods of teaching, and the sense of cooperation among students reached 0.8. e contradiction between popularity and improvement was solved, and the problem of differentiation and transformation of late-comers was solved [9].…”