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.
Highlights
With people’s widespread attention and a lot of research on feature selection algorithms, the filtering, encapsulation, and the hybrid model of the two in feature selection have been widely used in different fields
Encapsulation models based on swarm intelligence algorithms and hybrid filtering encapsulation models have gradually become research hotspots in feature selection algorithms
Feature selection is the process of selecting a subset of the original features from the data set
Summary
With people’s widespread attention and a lot of research on feature selection algorithms, the filtering, encapsulation, and the hybrid model of the two in feature selection have been widely used in different fields. A large number of features will seriously slow down the learning process of the entire algorithm when faced with a limited training data set. Is paper simulates the process of teacher-to-student teaching and learning between students and the process of mutual learning between students and improves students’ academic performance through the “teaching” of teachers and the mutual “learning” between students It has the advantages of few parameters, simple ideas, being easy to understand, strong robustness, and so forth, combining the swarm intelligence algorithm into the feature selection teaching model; in this way, the classification accuracy and efficiency can reach the best results, and the convergence speed of the algorithm is further improved. It reduces the possibility of the algorithm falling into a local optimum and can ensure that the algorithm achieves an optimal solution
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