Multi-Discriminator Deep Learning Framework for Comprehensive Classification Tasks

This paper presents an automated classification model leveraging multiple artificial intelligence discriminators to tackle complex classification tasks in industrial and agricultural scenarios. Traditional manual classification methods are often inefficient and susceptible to subjective biases. The proposed approach integrates deep learning techniques to extract multidimensional features of objects, combining discriminators such as decision trees, support vector machines, and random forests, to achieve high accuracy and efficiency. The study begins with dataset preprocessing and correlation analysis, followed by the construction of decision tree and random forest models, and concludes with parameter optimization to enhance classification performance. Experimental results show that the random forest model achieves superior performance with an accuracy of 84.07 % and an AUC value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 9 6}$</tex>. Furthermore, comparative analysis with other classifiers highlights the effectiveness of the proposed model. This research provides valuable insights and technical support for advancing intelligence and automation in various industrial applications.