The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction
Yıl: 2020 Cilt: 0 Sayı: 20 Sayfa Aralığı: 280 - 286 Metin Dili: İngilizce DOI: 10.31590/ejosat.773736 İndeks Tarihi: 03-12-2021
The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction
Öz: Machine learning has been used in many areas in recent years and has achieved quite successful results. Machine learning methods havebeen used from healthcare to driverless vehicles, it might also play a big role to increase productivity in the production sector. In thisstudy, we have compared the performance of some machine learning strategies on an abnormally distributed dataset. Any machinelearning methods can be easily applied to normally distributed data sets. However, it is necessary to alter the theoretical structure of thealgorithm or data transformation process while a dataset is abnormally distributed. In this regard, three different methodologies arecompared in this study. Initially, Support Vector Machines, which are often used in the literature, is used. Besides, Weighted SupportVector Machines, which is the revised version of the Support Vector Machines to produce successful results in abnormally distributeddata sets. Finally, the Synthetic Minority Oversampling Technique (SMOTE) is applied, and the distribution of the dataset wasartificially changed to normal distribution. Three techniques are compared in terms of sensitivity, specificity, precision, prevalence, F 1 score, and G-Mean evaluation criteria. Based on the results of the methods, Weighted Support Vector Machines produced the mostsuccessful results according to the chosen evaluation criteria.
Anahtar Kelime: Verimlilikte Yapay Zeka’nın Rolü: Şarap Kalitesinin Tahminine Yönelik Bir Vaka Çalışması
Öz: Yapay zeka son yıllarda birçok alanda kullanılmaya başlanmış ve oldukça başarılı sonuçlar elde edilmiştir. Sağlık sektöründensürücüsüz araçlara kadar birçok alanda kullanılan yapay zeka, üretim sektöründe de verimliliğin artırılması için sıklıkla kullanılmıştır.Bu çalışmada normal olarak dağılmamış bir veri setinde yapay zeka algoritmalarının kullanılmasına yönelik bir çerçeve çizilmeyeçalışılmıştır. Normal dağılım gösteren veri setlerinde herhangi bir yapay zeka algoritması kolaylıkla uygulanabilirken normal dağılımgöstermeyen veri setlerinde ya verinin kendisine farklı bir işlem uygulanması gerekir veya algoritmanın teorik yapısının revize edilmesigerekmektedir. Bu açıdan bu çalışmada iki farklı yöntemde uygulanmıştır. İlk olarak literatürde sıklıkla kullanılan Destek VektörMakinaları kullanılmıştır. Buna ek olarak Destek Vektör Makinalarının normal dağılmayan veri setlerinde başarılı sonuçlar vermesiiçin uyarlanmış şekli olan Ağırlıklandırılmış Destek Vektör Makineleri uygulanmıştır. Son olarak Sentetik Azınlık Aşırı ÖrneklemeTekniği (SMOTE) tekniği uygulanmış ve kullanılan veri seti yapay olarak normal dağılıma yakınsanmıştır. Kullanılan üç teknikteduyarlılık, hassaslık, özgüllük, yaygınlık, F skor ve Geometrik Ortalama (G-Mean) değerlendirme kriterleri açısından karşılaştırılmıştır.Çalışma sonucuna göre Ağırlıklandırılmış Destek Vektör Makineleri kullanılan değerlendirme kriterlerine göre en başarılı sonuçlarıvermiştir.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144–152.
- Chalfin, A., Danieli, O., Hillis, A., Jelveh, Z., Luca, M., Ludwig, J., & Mullainathan, S. (2016). Productivity and selection of human capital with machine learning. American Economic Review, 106(5), 124–127.
- Chawla, N. V, Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
- Chen, Q., Xu, J., & Koltun, V. (2017). Fast image processing with fully-convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, 2497–2506.
- Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009). Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems, 47(4), 547–553.
- Fang, R. (2006). Induction machine rotor diagnosis using support vector machines and rough set. International Conference on Intelligent Computing, 631–636.
- Jack, L. B., & Nandi, A. K. (2002). Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechanical Systems and Signal Processing, 16(2–3), 373–390.
- Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
- Manyika, J. (2017). A FUTURE THAT WORKS: AI, AUTOMATION, EMPLOYMENT, AND PRODUCTIVITY.
- Mondal, A., Ghosh, A., & Ghosh, S. (2018). Scaled and oriented object tracking using ensemble of multilayer perceptrons. Applied Soft Computing, 73, 1081–1094.
- Poyhonen, S., Negrea, M., Arkkio, A., Hyotyniemi, H., & Koivo, H. (2002). Fault diagnostics of an electrical machine with multiple support vector classifiers. Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium On, 373–378.
- Segatori, A., Marcelloni, F., & Pedrycz, W. (2018). On distributed fuzzy decision trees for big data. IEEE Transactions on Fuzzy Systems, 26(1), 174–192.
- Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., & Lanctot, M. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484.
- Sugumaran, V., Muralidharan, V., & Ramachandran, K. I. (2007). Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing, 21(2), 930–942.
- Ünlü, R., & Xanthopoulos, P. (2017). A weighted framework for unsupervised ensemble learning based on internal quality measures. Annals of Operations Research, 1–19.
- Ünlü, R., & Xanthopoulos, P. (2019). Estimating the number of clusters in a dataset via consensus clustering. Expert Systems with Applications.
- Veropoulos, K., Campbell, C., & Cristianini, N. (1999). Controlling the sensitivity of support vector machines. Proceedings of the International Joint Conference on AI, 55, 60.
- Xanthopoulos, P., & Razzaghi, T. (2014). A weighted support vector machine method for control chart pattern recognition. Computers & Industrial Engineering, 70, 134–149.
- Zhitong, C., Jiazhong, F., Hongpingn, C., Guoguang, H., & Ritchie, E. (2003). Support vector machine used to diagnose the fault of rotor broken bars of induction motors. Electrical Machines and Systems, 2003. ICEMS 2003. Sixth International Conference On, 2, 891–894.
| APA | ÜNLÜ R (2020). The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. , 280 - 286. 10.31590/ejosat.773736 |
| Chicago | ÜNLÜ RAMAZAN The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. (2020): 280 - 286. 10.31590/ejosat.773736 |
| MLA | ÜNLÜ RAMAZAN The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. , 2020, ss.280 - 286. 10.31590/ejosat.773736 |
| AMA | ÜNLÜ R The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. . 2020; 280 - 286. 10.31590/ejosat.773736 |
| Vancouver | ÜNLÜ R The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. . 2020; 280 - 286. 10.31590/ejosat.773736 |
| IEEE | ÜNLÜ R "The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction." , ss.280 - 286, 2020. 10.31590/ejosat.773736 |
| ISNAD | ÜNLÜ, RAMAZAN. "The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction". (2020), 280-286. https://doi.org/10.31590/ejosat.773736 |
| APA | ÜNLÜ R (2020). The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. Avrupa Bilim ve Teknoloji Dergisi, 0(20), 280 - 286. 10.31590/ejosat.773736 |
| Chicago | ÜNLÜ RAMAZAN The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. Avrupa Bilim ve Teknoloji Dergisi 0, no.20 (2020): 280 - 286. 10.31590/ejosat.773736 |
| MLA | ÜNLÜ RAMAZAN The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. Avrupa Bilim ve Teknoloji Dergisi, vol.0, no.20, 2020, ss.280 - 286. 10.31590/ejosat.773736 |
| AMA | ÜNLÜ R The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. Avrupa Bilim ve Teknoloji Dergisi. 2020; 0(20): 280 - 286. 10.31590/ejosat.773736 |
| Vancouver | ÜNLÜ R The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction. Avrupa Bilim ve Teknoloji Dergisi. 2020; 0(20): 280 - 286. 10.31590/ejosat.773736 |
| IEEE | ÜNLÜ R "The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction." Avrupa Bilim ve Teknoloji Dergisi, 0, ss.280 - 286, 2020. 10.31590/ejosat.773736 |
| ISNAD | ÜNLÜ, RAMAZAN. "The Role of Machine Learning in Productivity: A Case Study of WineQuality Prediction". Avrupa Bilim ve Teknoloji Dergisi 20 (2020), 280-286. https://doi.org/10.31590/ejosat.773736 |
