Research Article
Year 2019,
Volume: 2 Issue: 1, 18 - 27, 30.04.2019
Abstract
Teknolojinin ilerlemesi ve internetin gelişmesi ile beraber
günümüzde bilginin gücü de ön plana çıkmıştır. Bununla beraber internet
dünyasında bilgi kirliliği ve karmaşası ortaya çıkmaya başlamıştır. Bu
karmaşadan anlamlı verilerin çıkartılması ve yorumlanabilmesi için makine
öğrenmesi algoritmalarından yararlanılabilir. Bu çalışmada yazı formunda
girilen açıklamanın kategori bilgisine ulaşılması amaçlanmıştır. Bir e-ticaret
sitesinden ürün bilgileri etiketlenerek veri seti elde edilmiştir. Toplanan bu
veri seti makine öğrenmesi algoritmalarıyla model eğitimi gerçekleştirilmiş ve
9 farklı katagoriye ayırmak için doğru tahminleme yapması amaçlanmıştır. Bu
eğitim sırasında Random Forest, Karar Ağacı, Multinominal Naive Bayes
(Multinominal NB), Logistic Regression, Destek Vektör Makineleri (DVM) ve Yapay
Sinir Ağları (YSA) sınıflandırıcıları kullanılmış ve çıkan sonuçlar tablolarla
karşılaştırılmıştır.
Keywords
References
- [1] A. H. Aliwy ve E. H. Abdul Ameer, “Comparative Study of Five Text Classification Algorithms with their Improvements”, International Journal of Applied Engineering Research, 2017.
- [2] H. Alshalabi, S. Tiun, N. Omar, M. Albared, “Experiments on the Use of Feature Selection and Machine Learning Methods in Automatic Malay Text Categorization”, Science Direct, Procedia Technology, Elsevier, 2013.
- [3] I. Hmeidi, M. Al-Ayyoub, N. A. Abdulla, A. A. Almodawar, R. Abooraig, N. A. Mahyoub, “Automatic Arabic Text Categorisation: A Comprehensive Comparative Study”, Journal of Information Science, 2015. [4] C. C. Aggarwal ve C. X. Zhai, “A Survey Of Text Classification Algorithms”, Mining Text Data, Chapter 6, 2012.
- [5] H. Deng, Y. Sun, Y. Chang, J. Han, “Probabilistic Models for Classification” C.C. Aggarwal (Eds.), Data Classification Algorithms and Applications (pp. 67-70), CRC Press, New York, USA, 2015.
- [6] J. D. Rennie, L. Shih, J. Teevan, D. Karger, “Tackling the poor assumptions of naive bayes text classifiers” Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.
- [7] D. G. Kleinbaum, ve M. Klein, “Logistic Regression: A Self-Learning Text (Statistics for Biology and Health)”, Third Edition. New York: Springer 2010.
- [8] G. Louppe, “Understanding Random Forest”, doktora tezi, University of Liege, 2015.
- [9] S. C. Albright, W. L. Winston, ve C. Zappe, “Data Analysis & Decision Making”, Üçüncü Baskı, Australia: Thomson South-Western, 2006.
- [10] S. R. Gunn, “Support vector machines for classification and regression”, Technical Report, Faculty of Engineering, Science and Mathematics, School of Electronics and computer Science, 1998.
- [11] J.M. Zurada, “Introduction to Artificial Neural Systems”, West Publishing Company, 1992.
Year 2019,
Volume: 2 Issue: 1, 18 - 27, 30.04.2019
Abstract
With the advancement of technology and the development of the internet, the power of knowledge has come to the fore. However, in the internet world, information pollution and chaos started to emerge. Machine learning algorithms can be used to extract and interpret meaningful data from this complex. In this study, it is aimed to reach the category information of the explanation entered in the form of text. Product information from an e-commerce site was obtained by labeling the data set. This data set is modeled by machine learning algorithms and it is aimed to make accurate estimation to divide into 9 different categories. During this training, Random Forest, Decision Tree, Multinominal Naive Bayes (Multinominal NB), Logistic Regression, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) classifiers were used and the results were compared with the tables.
Keywords
References
- [1] A. H. Aliwy ve E. H. Abdul Ameer, “Comparative Study of Five Text Classification Algorithms with their Improvements”, International Journal of Applied Engineering Research, 2017.
- [2] H. Alshalabi, S. Tiun, N. Omar, M. Albared, “Experiments on the Use of Feature Selection and Machine Learning Methods in Automatic Malay Text Categorization”, Science Direct, Procedia Technology, Elsevier, 2013.
- [3] I. Hmeidi, M. Al-Ayyoub, N. A. Abdulla, A. A. Almodawar, R. Abooraig, N. A. Mahyoub, “Automatic Arabic Text Categorisation: A Comprehensive Comparative Study”, Journal of Information Science, 2015. [4] C. C. Aggarwal ve C. X. Zhai, “A Survey Of Text Classification Algorithms”, Mining Text Data, Chapter 6, 2012.
- [5] H. Deng, Y. Sun, Y. Chang, J. Han, “Probabilistic Models for Classification” C.C. Aggarwal (Eds.), Data Classification Algorithms and Applications (pp. 67-70), CRC Press, New York, USA, 2015.
- [6] J. D. Rennie, L. Shih, J. Teevan, D. Karger, “Tackling the poor assumptions of naive bayes text classifiers” Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.
- [7] D. G. Kleinbaum, ve M. Klein, “Logistic Regression: A Self-Learning Text (Statistics for Biology and Health)”, Third Edition. New York: Springer 2010.
- [8] G. Louppe, “Understanding Random Forest”, doktora tezi, University of Liege, 2015.
- [9] S. C. Albright, W. L. Winston, ve C. Zappe, “Data Analysis & Decision Making”, Üçüncü Baskı, Australia: Thomson South-Western, 2006.
- [10] S. R. Gunn, “Support vector machines for classification and regression”, Technical Report, Faculty of Engineering, Science and Mathematics, School of Electronics and computer Science, 1998.
- [11] J.M. Zurada, “Introduction to Artificial Neural Systems”, West Publishing Company, 1992.
There are 10 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | April 30, 2019 |
| Submission Date | February 6, 2019 |
| Acceptance Date | April 24, 2019 |
| Published in Issue | Year 2019Volume: 2 Issue: 1 |
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