Classification of different forest types with machine learning algorithms

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Tarih

2016

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Yayıncı

Latvia Univ Life Sciences & Technologies

Erişim Hakkı

info:eu-repo/semantics/closedAccess

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

Anahtar Kelimeler

Forest Types, Multilayer Perceptron, K-NN Classifier, Data Mining

Kaynak

Research For Rural Development 2016, Vol. 1

WoS Q Değeri

N/A

Scopus Q Değeri

N/A

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