Classification of Different Wheat Varieties by Using Data Mining Algorithms

Authors

  • Kadir Sabancı Karamanoğlu Mehmetbey Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
  • Mustafa Akkaya Karamanoglu Mehmetbey University

DOI:

https://doi.org/10.18201/ijisae.62843

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 2001

Y. Lisans Elektrik Elektronik Müh. Selçuk Üniversitesi 2005

Doktora Tarım Makineleri Selçuk Üniversitesi 2012

Mustafa Akkaya, Karamanoglu Mehmetbey University

Faculty of Engineering Department of Energy Systems Engineering

Karaman,Turkey 

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Published

27.05.2016

How to Cite

[1]
K. Sabancı and M. Akkaya, “Classification of Different Wheat Varieties by Using Data Mining Algorithms”, Int J Intell Syst Appl Eng, vol. 4, no. 2, pp. 40–44, May 2016.

Issue

Section

Research Article