The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2016, Vol 4, Issue 2
Abstract
In this study, the Japanese Oak and Pine Wilt in forested areas of Japan was classified into two group as diseased trees and all other land cover area according to the 6 attributes in the spectral data set of the forest. The Wilt Data Set which was obtained from UCI machine learning repository database was used. Weka (Waikato Environment for Knowledge Analysis) software was used for classification of areas in the forests. The classification success rates and error values were calculated and presented for classification data mining algorithms just as Multilayer Perceptron (MLP) and k-Nearest Neighbor (kNN). In MLP neural networks the classification performance for various numbers of neurons in the hidden layer was presented. The highest success rate was obtained as 86.4% when the number of neurons in the hidden layer was 10. The classification performance of kNN method was calculated for various counts of neighborhood. The highest success rate was obtained as 72% when the count of neighborhood number was 2.
Authors and Affiliations
Muhammed Fahri Unlersen *| Selcuk University Doğanhisar Vocational School Konya Turkey, Kadir Sabanci| Karamanoglu Mehmetbey University Electrical and Electronics Engineering Engineering Department Karaman Turkey
Keywords
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- EP ID EP794
- DOI 10.18201/ijisae.05552
- Views 445
- Downloads 23
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