Classification of Different Wheat Varieties by Using Data Mining Algorithms

Yıl 2016, Cilt: 4 Sayı: 2, 40 - 44, 27.05.2016

Kadir Sabancı , Mustafa Akkaya

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

Cited By: 15

Öz

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.

 

Anahtar Kelimeler

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

Kaynakça

• Gökhan Silahtaroğlu (2013). Data Mining Concepts And Algorithms, Papatya Education Publishing.
• T. C. Sharma and M. Jain (2013). “WEKA Approach For Comparative Study Of Classification Algorithm”, International Journal Of Advanced Research In Computer And Communication Engineering Vol. 2, Issue 4.
• Geoffrey Holmes, Andrew Donkin, and Ian H. Witten (2002). Weka: A Machine Learning Workbench “Department Of Computer Science University Of Waikato, Hamilton, New Zealand”.
• Omid Mahmoud (2011). Design Of An Expert System For Sorting Pistachio Nuts Through Decision Tree And Fuzzy Logic Classifier, Expert Systems With Applications 38, 4339-4347.
• G.Silvia Ceballos-Magana, De Pablos Fernando, Jurado Jose Marcos, Martin Jesus Maria, Alcazar Angela, Valencia-Muniz Roberto and Lumbreras Gonzalo Raquel (2011). Hornillos Izquierdo Roberto, Characterisation Of Tequila According To Their Major Volatile Composition Using Multilayer Perceptron Neural Networks, Food Chemistry Volume 136, Issues 3–4, 1–15, Pages 1309–1315 ASSET .
• Emanuelle Morais De Oliveira, Dimas Samid Leme,
• Bruno Henrique Groenner Barbosa Mirian Pereira Rodarte and Rosemary Gualberto Fonseca Alvarenga Pereira (2016). A Computer Vision System For Coffee Beans Classification Based On Computational Intelligence Techniques, Journal Of Food Engineering Volume 171, Pages 22–27.
• T. Karthikeyan and P. Thangaraju (2015). Best First and Greedy Search Based CFS- Naïve Bayes Classification Algorithms For Hepatitis Diagnosis, Biosciences Biotechnology Research Asia, Vol. 12(1), 983-990.
• Nowakowski, K. (2009). Inst. Of Agric. Eng., Poznah Univ. Of Life Sci., Poznan, Poland ; Boniecki, P. ; Dach, J. The Identification Of Mechanical Damages Of Kernels Basis On Neural Image Analysis, Digital Image Processing, 2009 International Conference On, 978-0-7695-3565-4, Page 412 – 415.
• Daniel Alves Aguiar, Marcos Adami, Wagner Fernando Silva, Bernardo Friedrich Theodor Rudorff, Marcio Pupin Mello and João Dos Santos Vila Da Silva (2010), Modis Time Series To Assess Pasture Land, Geoscience And Remote Sensing Symposium (IGARSS), 2010 IEEE International, Page 2123 – 2126, 25-30.
• UCI,Https://Archive.Ics.Uci.Edu/Ml/Datasets.Html Las Access: 22.12.2015
• M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik and S. Zak (2010). 'A Complete Gradient Clustering Algorithm For Features Analysis Of X-Ray Images', In: Information Technologies In Biomedicine, Ewa Pietka, Jacek Kawa (Eds.), Springer-Verlag, Berlin-Heidelberg, Pp. 15-24.
• WEKA, Http://Www.Cs.Waikato.Ac.Nz/~Ml/Weka/ Last Access: 19.12.2015.
• Roberto Muniz-Valencia, Jose M.Jurado, Silvia G. Ceballos-Magana and Angela Alcazar (2014). Characterization Of Mexicon Coffee According To Mineral Contents By Means Of Multilayer Perceptrons Artificial Neural Networks, Journal Of Food Composition And Analysis 34, 7-11.
• Sibel K. Çalışkan and İbrahim Soğukpınar (2012). Knn: K-Means And Methods K Nearest Neighbor Determination Of The Adoption Network.
• Cengiz Coşkun (2010). Data Mining Algorithm And Classification, Master’ S Thesis.
• Emanuelle Morais De Oliveira, Dimas Samid Leme,
• Bruno Henrique Groenner Barbosa Mirian Pereira Rodarte and Rosemary Gualberto Fonseca Alvarenga Pereira (2016). A Computer Vision System For Coffee Beans Classification Based On Computational Intelligence Techniques, Journal Of Food Engineering Volume 171, Pages 22–27 .