Research on an Integrated Intelligent Classification Algorithm Based on K-Means PCA-RF Machine Learning

Abstract

With the rapid development of machine learning and artificial intelligence, research on classification models is gradually becoming popular. This article aims to propose a general classification model and classify indicator features. First, this paper constructs the data preprocessing based on K-Means, and data dimensionality reduction based on PCA algorithm. Finally, random forest algorithm (RF) is used for feature classification, and 325 groups of data are used for training. The results show that: (1) The K-Means PCA-RF algorithm constructed in this paper has good robustness and classification performance. (2) K-Means PCA-RF can effectively classify features and perform sensitivity analysis.