A Review of the Principles and Applications of Mainstream Classification Algorithms: From Logistic Regression to Neural Networks
DOI:
https://doi.org/10.54097/3esa7m26Keywords:
Classification Algorithm, Logistic Regression, K-nearest Neighbor, Decision Tree, Neural NetworksAbstract
This article illustrates four mainstream classification algorithms: logistic regression systematically, K-nearest neighbors (KNN), decision trees, and neural networks. Analyzed the mathematical principles, core calculation formulas, and basic achieving methods of each of these four kinds of algorithms; From the perspective of practical application scenes, analyze the applicability and obvious limitations of these algorithms when facing different data. This study provides a systematic proposal for the theoretical understanding and practical application of classification models in engineering and daily life.
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[1] Li, G. J. (2024). Big data and computational models. Big Data, (1), 9–16. (in Chinese)
[2] Zhang, M. J., & Guo, X. A. (2025). The "order" of technology: Classification logic of intelligent algorithms and "substitution by generalization". Modern Publishing, (7), 51–63. (in Chinese)
[3] Wu, T. (2000). Classification and bifurcation: An exploration of the self-organizing origins of knowledge and scientific knowledge — Taking the evolution of classification and the shift of cultural perspective on classification in biology as an example. Journal of Dialectics of Nature, (6), 25–33. (in Chinese)
[4] Meng, L., & Liu, J. P. (2022). Research on classification algorithms in data mining. China Education Network, (7), 43–45. (in Chinese)
[5] Xu, Y., & Qu, D. Q. (2024). Design and implementation of computer network intrusion detection system based on decision tree classification algorithm. Information Recording Materials, 25(6), 137–139. (in Chinese)
[6] Li, X. M. (2020). Algorithms in classification problems. Chinese Information Technology Education, (23), 24–28. (in Chinese)
[7] Deng, J. Y. (2019). Research and application of multinomial logistic regression algorithm in the context of big data [Master’s thesis, Wuhan Institute of Technology]. (in Chinese)
[8] Liao, L., Chen, Y. Y., Zeng, G. F., & Liu, P. Q. (2025). Logistic regression classification method based on granular computing. Computer Simulation, 42(2), 382–388. (in Chinese)
[9] Xia, M. (2024). Research and application of fusion algorithm of decision tree and logistic regression model in binary classification problems [Master’s thesis, University of International Business and Economics]. (in Chinese)
[10] Tang, T., & Li, M. (2022). Analysis and research on logistic regression algorithm in the context of big data. Electronic Technology & Software Engineering, (17), 177–181. (in Chinese)
[11] Han, L. R. (2019). Research on k-nearest neighbor and reverse k-nearest neighbor query based on comprehensive evaluation in obstacle space [Master’s thesis, Yanshan University]. (in Chinese)
[12] Liu, X. M., Li, C. Z. X., Wu, S. C., Zhang, Y. C., Bai, H. Y., Cheng, Z. H., Chen, Z., Li, Y. F., Lan, Y., & Shen, C. (2024). Review on text classification algorithms and their application scenarios. Chinese Journal of Computers, 47(6), 1244–1287. (in Chinese)
[13] Cheng, Y. F. (2021). Overview of data classification algorithms in data mining. Digital Communication World, (2), 136–160. (in Chinese)
[14] Wang, Y. L. (2022). Research on machine learning algorithm classification based on artificial intelligence. Computer Fan (Popular Edition), (7), 22–24. (in Chinese)
[15] Lü, G. F., Tan, J., Qiao, G. J., & Yan, J. L. (2022). Decision tree algorithm for message classification. Journal of National University of Defense Technology, 44(3), 184–193. (in Chinese)
[16] Jiang, S. Q. (2025). Research on supervised learning algorithm of deep spiking neural network and its application [Doctoral dissertation, Xidian University]. (in Chinese)
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