Utilizing ensemble learning methods for the classification of forest cover types

D. T. Muhamediyeva; L. U. Safarova; S. X. Eshankulov

doi:10.1117/12.3025074

20 February 2024 Utilizing ensemble learning methods for the classification of forest cover types

D. T. Muhamediyeva, L. U. Safarova, S. X. Eshankulov

Proceedings Volume 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023); 130650X (2024) https://doi.org/10.1117/12.3025074
Event: Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 2023, Dushanbe, Tajikistan

Abstract

This work is devoted to the study and application of ensemble methods in the problem of classifying forest cover types based on the "covtype" data set. The paper examines two popular ensemble methods: random forest and gradient boosting . First, data analysis and preprocessing is carried out, including dividing the sample into training and test sets. Then random forest and gradient boosting models are built on the training set. F1-measures, as well as ROC AUC. Results of study shows that both ensemble methods effectively cope with the task of classifying forest cover types. The resulting metrics confirm the high accuracy and ability of the models to generalize to new data. An important step in the research is to compare the performance of random forest and gradient boosting . The work also includes visualization of results such as ROC curves for further exploration and comparison of the two methods. The findings can be useful for choosing the best method in specific scenarios and understanding their applicability in natural data classification problems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

D. T. Muhamediyeva, L. U. Safarova, and S. X. Eshankulov "Utilizing ensemble learning methods for the classification of forest cover types", Proc. SPIE 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 130650X (20 February 2024); https://doi.org/10.1117/12.3025074

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