Classification of Different Wheat Varieties by Using Data Mining Algorithms

  • Kadir Sabancı Karamanoğlu Mehmetbey Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
  • Mustafa Akkaya Karamanoglu Mehmetbey University
Keywords: WEKA, Data mining, Multilayer perceptron, KNN, J48, Naive Bayes

Abstract

There are various applications using computer-aided quality controlling system. In this study, seed data set acquired from UCI machine learning database was used. The purpose of the study is to perform the operations for separation of seed species from each other in the seed data set. Three different seed whose data was acquired from the UCI machine learning database was used. Later it was classified by applying the methods of KNN, Naive Bayes, J48 and multilayer perceptron to the dataset. While wheat seed data received from the UCI machine learning database was classified, WEKA program was used. Depending on the number of neurons the highest classification success came in 7-layer neurons. Our success rate for the number of 7-layer neurons came to 97.17% When the classification success rate was calculated according to KNN for the values of different neighbour, the highest success rate for neighbour was set at 95.71% for 4. Neighbour. With this method, classification of seeds depending on their properties was provided more quickly and effectively. 

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Author Biographies

Kadir Sabancı, Karamanoğlu Mehmetbey Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
Lisans Elektrik Elektronik Müh. Selçuk Üniversitesi 2001Y. Lisans Elektrik Elektronik Müh. Selçuk Üniversitesi 2005Doktora Tarım Makineleri Selçuk Üniversitesi 2012
Mustafa Akkaya, Karamanoglu Mehmetbey University
Faculty of Engineering Department of Energy Systems Engineering Karaman,Turkey 
Published
2016-05-27
How to Cite
[1]
K. Sabancı and M. Akkaya, “Classification of Different Wheat Varieties by Using Data Mining Algorithms”, IJISAE, vol. 4, no. 2, pp. 40-44, May 2016.
Section
Research Article