Classification of different forest types with machine learning algorithms
Yükleniyor...
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Latvia Univ Life Sciences & Technologies
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, forest type mapping data set taken from UCI (University of California, Irvine) machine learning repository database has been classified using different machine learning algorithms including Multilayer Perceptron, k-NN, J48, Naive Bayes, Bayes Net and KStar. In this dataset, there are 27 spectral values showing the type of three different forests (Sugi, Hinoki, mixed broadleaf). As the performance measure criteria, the classification accuracy has been used to evaluate the classifier algorithms and then to select the best method. The best classification rates have been obtained 90.43% with MLP, and 89.1013% with k-NN classifier (for k=5). As can be seen from the obtained results, the machine learning algorithms including MLP and k-NN classifier have obtained very promising results in the classification of forest type with 27 spectral features.
Açıklama
22nd Annual International Scientific Conference on Research for Rural Development -- MAY 18-20, 2016 -- Latvia Univ Agr, Jelgava, LATVIA
WOS:000391253000041
WOS:000391253000041
Anahtar Kelimeler
Forest Types, Multilayer Perceptron, k-NN Classifier, Data Mining
Kaynak
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Sabancı, K., Ünlersen, M. F., Polat, K., 22nd Annual ınternational scientific conference research for rural development, 2016. (2016). Classification of different forest types with machine learning algorithms. Research for Rural Development, 1, 254-260.
