INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION

Ümit Yılmaz; Özlem Kuvat

doi:10.17482/uumfd.1296479

Research Article

INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION

Year 2023, Volume: 28 Issue: 2, 437 - 452, 31.08.2023

Ümit Yılmaz , Özlem Kuvat

https://doi.org/10.17482/uumfd.1296479

Cited By: 2

Abstract

Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment’s performance. This research’s aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R2 value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R2 values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R2 value among those that selected the fewest features.

Keywords

Feature selection, machine learning, overall equipment effectiveness

References

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Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Year 2023, Volume: 28 Issue: 2, 437 - 452, 31.08.2023

Ümit Yılmaz , Özlem Kuvat

https://doi.org/10.17482/uumfd.1296479

Cited By: 2

Abstract

Toplam ekipman etkinliği (TEE); kullanılabilirliği, performansı ve kaliteyi birleştirerek üretim etkinliğini tanımlamaktadır ve üretim ekipmanının performansını değerlendirmek için kullanılmaktadır. Bu araştırmanın amacı, bir kutu fabrikasının oluklu mukavva departmanının TEE’sinin başarılı bir şekilde tahmin etmede, öznitelik seçim tekniklerinin ve makine öğrenmesi tekniklerinden biri olan çoklu doğrusal regresyon yönteminin potansiyelini araştırmaktır. Çalışmada girdi öznitelikleri olarak altı farklı planlı duruş süresi ve onyedi farklı gerçekleşecek faaliyetlere ilişkin önceden bilinen kavramlara ilişkin bilgiler kullanılmıştır. Ayrıca veri kümesinde en ayırt edici özellik alt kümesini bulmak için geriye doğru eleme, ileri doğru seçim, adımsal seçim, korelasyon tabanlı öznitelik seçim, genetik algoritma, rastgele orman, ekstra ağaç, ridge regresyon, lasso regresyon ve elastik net öznitelik seçim yöntemlerinden faydalanılmıştır. 23 öznitelikten, 1 çıktıdan ve 1204 iş günlük bilgiden oluşan veri seti üzerinde yapılan analizler neticesinde 19 adet öznitelik seçen elastik net – çoklu doğrusal regresyon modeli, geliştirilen diğer modellere kıyasla en iyi ortalama R2 değerini vermiştir. Elde edilen ortalama R2 değerleri arasında çok büyük bir fark olmaması dolayısıyla Occam’ın usturası ilkesi dikkate alınmıştır. İlkeye göre geliştirilen modellerden en az öznitelik seçenler arasında en iyi R2 değerini stepwise selection - çoklu doğrusal regresyon modeli vermiştir.

Keywords

Öznitelik seçimi, makine öğrenmesi, toplam ekipman etkinliği

References

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7. Bai, H., Liu, P., Fu, X., Qiao, L., Liu, C., Xin, Y. and Ling, Z. (2023) Application of elastic net in quantitative analysis of major elements using Martian laser-induced breakdown spectroscopy datasets, Spectrochimica Acta Part B: Atomic Spectroscopy, 199, 106587. doi:10.1016/j.sab.2022.106587
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13. Corrales, D. C., Schoving, C., Raynal, H., Debaeke, P., Journet, E.-P. and Constantin, J. (2022) A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France, Computers and Electronics in Agriculture, 192, 106578. doi:10.1016/j.compag.2021.106578
14. da Costa, N. L., de Lima, M. D. and Barbosa, R. (2022) Analysis and improvements on feature selection methods based on artificial neural network weights, Applied Soft Computing, 127, 109395. doi:10.1016/j.asoc.2022.109395
15. Emanet, S., Karatas Baydogmus, G. and Demir, O. (2021) Öznitelik seçme yöntemlerinin makine öğrenmesi tabanlı saldırı tespit sistemi performansına etkileri, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(5), 743-755. doi:10.24012/dumf.1051340
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17. Eroğlu, D. Y. (2019) Systematization, Implementation and Analysis of the Overall Throughput Effectiveness Calculation for the Finishing Processes after Weaving, Journal of Textile & Apparel/Tekstil ve Konfeksiyon, 29(2). doi:10.32710/tekstilvekonfeksiyon.457170
18. Erturan, A. M., Karaduman, G. and Durmaz, H. (2023) Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection, Journal of Hazardous Materials, 455, 131616. doi:10.1016/j.jhazmat.2023.131616
19. Fathima, M. D., Samuel, S. J., Natchadalingam, R. and Kaveri, V. V. (2022) Majority voting ensembled feature selection and customized deep neural network for the enhanced clinical decision support system, International Journal of Computers and Applications, 44(10), 991-1001. doi:10.1080/1206212X.2022.2069643
20. Fletcher, L., Akhtar, N., Zhan, X., Jafarikia, M., Sullivan, B. P., Huber, L.-A. and Li, J. (2022) Identification of Candidate Salivary, Urinary and Serum Metabolic Biomarkers for High Litter Size Potential in Sows (Sus scrofa), Metabolites, 12(11), 1045. doi:10.3390/metabo12111045
21. Genç, İ. and Vupa Çilengiroğlu, Ö. (2021, June 4-6, 2021). Toplam Ekipman Etkinliği Puanının Lojistik ve Karar Ağacı Algoritmaları ile Modellenmesi. International Conference on Data Science & Applications, Online.
22. Gunes, H., Coramik, M., Bicakci, S., Citak, H. and Ege, Y. (2022) Crack identification system on MOH cold rolled grain oriented sheets: Application of K-fold cross validated BRANN, Measurement, 195, 111128. doi:10.1016/j.measurement.2022.111128
23. Jamei, M., Karbasi, M., Alawi, O. A., Kamar, H. M., Khedher, K. M., Abba, S. I. and Yaseen, Z. M. (2022) Earth skin temperature long-term prediction using novel extended Kalman filter integrated with Artificial Intelligence models and information gain feature selection, Sustainable Computing: Informatics and Systems, 35, 100721. doi:10.1016/j.suscom.2022.100721
24. Korkmaz, G. and Eroğlu, E. (2020) Model karmaşıklığının kontrolü, İktisadi ve İdari Yaklaşımlar Dergisi, 2(2), 146-162. doi:10.47138/jeaa.780031
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There are 59 citations in total.

Details

Primary Language	English
Subjects	Industrial Engineering
Journal Section	Research Articles
Authors	Ümit Yılmaz 0000-0003-4268-8598 Özlem Kuvat 0000-0001-7017-4557
Early Pub Date	August 18, 2023
Publication Date	August 31, 2023
Submission Date	May 12, 2023
Acceptance Date	July 13, 2023
Published in Issue	Year 2023 Volume: 28 Issue: 2

Cite

APA	Yılmaz, Ü., & Kuvat, Ö. (2023). INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 28(2), 437-452. https://doi.org/10.17482/uumfd.1296479
AMA	Yılmaz Ü, Kuvat Ö. INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION. UUJFE. August 2023;28(2):437-452. doi:10.17482/uumfd.1296479
Chicago	Yılmaz, Ümit, and Özlem Kuvat. “INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28, no. 2 (August 2023): 437-52. https://doi.org/10.17482/uumfd.1296479.
EndNote	Yılmaz Ü, Kuvat Ö (August 1, 2023) INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28 2 437–452.
IEEE	Ü. Yılmaz and Ö. Kuvat, “INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION”, UUJFE, vol. 28, no. 2, pp. 437–452, 2023, doi: 10.17482/uumfd.1296479.
ISNAD	Yılmaz, Ümit - Kuvat, Özlem. “INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28/2 (August 2023), 437-452. https://doi.org/10.17482/uumfd.1296479.
JAMA	Yılmaz Ü, Kuvat Ö. INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION. UUJFE. 2023;28:437–452.
MLA	Yılmaz, Ümit and Özlem Kuvat. “INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 28, no. 2, 2023, pp. 437-52, doi:10.17482/uumfd.1296479.
Vancouver	Yılmaz Ü, Kuvat Ö. INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION. UUJFE. 2023;28(2):437-52.

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