Research on Comparisons with three survival models Comparisons with Decision Tree, Random forests and CNNs
DOI:
https://doi.org/10.61173/pygdcc73Keywords:
Decision tree, random forests, CNN, classification algorithmAbstract
Decision tree, random forests and CNN are three advanced algorithms that can solve classification tasks. This article will compare the performance of the three different algorithms in classification tasks. Here, CNN extracts features through convolution operations to detect objects or recognize images, other two achieve this through building a tree structure. They could help to solve so many problems. Decision tree have been widely applied in fields such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. CNN can be used to detect individual plants or pixels as well as for vegetation classification. Nevertheless, they still encounter challenges in the classification tasks, such as low classification accuracy.
References
[1] Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 53. https://doi.org/10.1186/s40537-021-00444-6
[2] Chauhan, R., Ghanshala, K. K., & Joshi, R. C. (2018). Convolutional neural network (CNN) for image detection and recognition. In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 278– 282). IEEE. https://doi.org/10.1109/ICSCCC.2018.8703283
[3] Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24–49. https://doi.org/10.1016/ j.isprsjprs.2020.12.010
[4] Belgiu, M., & Drăgut, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
[5] Paul, A., Mukherjee, D. P., Das, P., Gangopadhyay, A., Chintha, A. R., & Kundu, S. (2018). Improved random forest for classification. IEEE Transactions on Image Processing, 27(8), 4012–4023. https://doi.org/10.1109/TIP.2018.2835141
[6] Biau, G., & Scornet, E. (2015). A random forest guided tour. arXiv preprint arXiv:1511.05741. https://arxiv.org/ abs/1511.05741
[7] Karita, S., Chen, N., Hayashi, T., Hori, T., Inaguma, H., Jiang, Z., Someki, M., Soplin, N. E. Y., Yamamoto, R., Wang, X., Watanabe, S., Yoshimura, T., & Zhang, W. (2019). A comparative study on transformer vs RNN in speech applications. arXiv preprint arXiv:1909.06317. https://doi. org/10.48550/arXiv.1909.06317
[8] Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https:// doi.org/10.1016/j.physd.2019.132306
[9] Yin, W., Kann, K., Yu, M., & Schütze, H. (2017). Comparative study of CNN and RNN for natural language processing. arXiv preprint arXiv:1702.01923. https://doi. org/10.48550/arXiv.1702.01923
[10] Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics, 18(5), 275–285. https://doi. org/10.1002/cem.873
[11] Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674. https://doi. org/10.1109/21.97458
Downloads
Published
Issue
Section
License
Copyright (c) 2026 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
