A New Approach Towards Identification of Individuals Belonging to Different Subpopulations Using Machine Learning Models and CLC Data
Fragment książki (Materiały konferencyjne)
MNiSW
200
konferencja
| Status: | |
| Autorzy: | Charytanowicz Małgorzata, Perzanowski Kajetan, Januszczak Maciej, Wołoszyn-Gałęza Aleksandra, Sobczuk Maria , Kulczycki Piotr |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 748 - 755 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 24th IEEE International Conference on Data Mining Workshops |
| Skrócona nazwa konferencji: | 24th ICDMW 2024 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 9 grudnia 2024 do 9 grudnia 2024 |
| Miasto konferencji: | Abu Dhabi |
| Państwo konferencji: | ZJEDNOCZONE EMIRATY ARABSKIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Spatial distributions of wisents (European bison) Bison bonasus (Linnaeus 1758), were studied in the Bieszczady Mountains (south-eastern Poland) on the basis of telemetric data obtained from two subpopulations: the western – inhabiting the forest districts of Baligród, Komańcza, Cisna and Lesko, and the eastern – dwelling within the districts of Lutowiska, Stuposiany, and in Bieszczadzki National park. Data was collected between 2002-2021. In our study, we propose a novel approach for classifying wisent subpopulations that utilizes machine learning methods and CLC data. For this purpose, the performances of eight algorithms: Naive Bayes, Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, Extreme Gradient Boosting, k-Nearest Neighbors and Decision Tree were investigated. The algorithms were compared according to the following indicators: accuracy, Cohen’s-Kappa, precision, recall and F1 score. Their assessment was enhanced through the application of statistical inference (the Friedman test with post-hoc analysis) and SHAP values. The lowest results were achieved by Naive Bayes and Logistic Regression methods (accuracy of 73.69% and 74.43%, respectively), whilst significantly higher results were achieved by eXtreme Gradient Boosting, k-Nearest Neighbors and Decision Tree classifiers, with the classification accuracy exceeding 90% (91.81%, 92.8%, 93.47%, respectively). Based on the results, we conclude that CLC data represents a valuable source of information regarding the affinity of wisents towards different habitat conditions. |
