The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery

  • Muhammed Fahri Unlersen Selcuk University
  • Kadir Sabanci Karamanoglu Mehmetbey University
Keywords: k - Nearest Neighbor, Multilayer Perceptron Neural Network, Weka, Classification, Remote Sensing

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.

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Author Biographies

Muhammed Fahri Unlersen, Selcuk University
Computural Programming
Kadir Sabanci, Karamanoglu Mehmetbey University
Electrical and Electronics Eng. Dep.The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery
Published
2016-05-27
How to Cite
[1]
M. Unlersen and K. Sabanci, “The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery”, IJISAE, vol. 4, no. 2, pp. 25-28, May 2016.
Section
Research Article