The data feature selection method based on optimization algorithms and its application in machine learning

This paper studies the feature selection method based on optimization algorithm and its application in machine learning, aiming to improve model performance and reduce computational complexity. The study first analyzes the application of traditional optimization algorithm, intelligent optimization algorithm and hybrid optimization algorithm in feature selection, and proposes a feature selection strategy based on particle swarm optimization. Experimental results show that the optimization algorithm can significantly reduce the number of features and improve the prediction accuracy of classification and regression tasks. For example, the accuracy is increased to 0.93 through iterative optimization. Further analysis shows that the optimization algorithm can effectively identify high-contribution features and enhance the explanatory power of the model through global search and dynamic adjustment. This paper provides an efficient feature selection scheme for high-dimensional data processing and provides new ideas for optimizing machine learning performance.