Classification of Different Wheat Varieties by Using Data Mining Algorithms

Kadir Sabancı, Mustafa Akkaya
  • Kadir Sabancı
    Karamanoğlu Mehmetbey Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü, Turkey | kadirsabanci@yandex.com
  • Mustafa Akkaya
    Karamanoglu Mehmetbey University, Turkey

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.

 

Keywords

WEKA; Data mining; Multilayer perceptron; KNN; J48; Naive Bayes

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Submitted: 2017-02-21 18:50:38
Published: 2016-05-27 00:00:00
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