Comparison of Machine Learning Based on Category Theory

In recent years, machine learning has been widely used in data analysis of network engineering. The increasing types of model and data enhance the complexity of machine learning. In this paper, we propose a mathematical structure based on category theory as a combination of machine learning that combines multiple theories of data mining. We aim to study machine learning from the perspective of classification theory. Category theory utilizes mathematical language to connect the various structures of machine learning. We implement the representation of machine learning with category theory. In the experimental section, slice categories and functors are introduced in detail to model the data preprocessing. We use functors to preprocess the benchmark dataset and evaluate the accuracy of nine machine learning models. A key contribution is the representation of slice categories. This study provides a structural perspective of machine learning and a general method for the combination of category theory and machine learning.