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

Yıl 2023, Cilt: 28 Sayı: 2, 437 - 452, 31.08.2023

Ümit Yılmaz , Özlem Kuvat

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

Cited By: 2

Öz

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.

Anahtar Kelimeler

Feature selection, machine learning, overall equipment effectiveness

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Öz

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.

Anahtar Kelimeler

Öznitelik seçimi, makine öğrenmesi, toplam ekipman etkinliği

Kaynakça

• 1. Adak, M. F. and Duralioğlu, Ö. (2023) Makine Öğrenmesi Yöntemleri Kullanılarak Öğrencilerin Kazanım Bilgileri ile Sınavlardaki Başarı Durumunun Tahmini, Journal of Intelligent Systems: Theory and Applications, 6(1), 43-51. doi:10.38016/jista.1183353
• 2. Akman, D. V., Malekipirbazari, M., Yenice, Z. D., Yeo, A., Adhikari, N., Wong, Y. K., Abbasi, B. and Gumus, A. T. (2023) k-best feature selection and ranking via stochastic approximation, Expert Systems with Applications, 213, 118864. doi:10.1016/j.eswa.2022.118864
• 3. Almaghrabi, F., Xu, D.-L. and Yang, J.-B. (2021) An evidential reasoning rule based feature selection for improving trauma outcome prediction, Applied Soft Computing, 103, 107112. doi:10.1016/j.asoc.2021.107112
• 4. Amini, F. and Hu, G. (2021) A two-layer feature selection method using Genetic Algorithm and Elastic Net, Expert Systems with Applications, 166, 114072. doi:10.1016/j.eswa.2020.114072
• 5. Austin, P. C. and Tu, J. V. (2004) Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality, Journal of Clinical Epidemiology, 57(11), 1138-1146. doi:10.1016/j.jclinepi.2004.04.003
• 6. Aydın, F. (2022) A New Instance Selection Method for Enlarging Margins Between Classes, Journal of Intelligent Systems: Theory and Applications, 5(2), 119-126. doi:10.38016/jista.1033354
• 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
• 8. Barbosa, B. D. S., Ferraz, G. A. e. S., Costa, L., Ampatzidis, Y., Vijayakumar, V. and dos Santos, L. M. (2021) UAV-based coffee yield prediction utilizing feature selection and deep learning, Smart Agricultural Technology, 1, 100010. doi:10.1016/j.atech.2021. 100010
• 9. Chamlal, H., Ouaderhman, T. and Aaboub, F. (2022) A graph based preordonnances theoretic supervised feature selection in high dimensional data, Knowledge-Based Systems, 257, 109899. doi:10.1016/j.knosys.2022.109899
• 10. Chaudhari, K. and Thakkar, A. (2023) Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction, Expert Systems with Applications, 219, 119527. doi:10.1016/j.eswa.2023.119527
• 11. Chicco, D., Warrens, M. J. and Jurman, G. (2021) The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Computer Science, 7, e623. doi:10.7717/peerj-cs.623
• 12. Chikwendu, O. C., Chima, A. S. and Edith, M. C. (2020) The optimization of overall equipment effectiveness factors in a pharmaceutical company, Heliyon, 6(4), e03796. doi:10.1016/j.heliyon.2020.e03796
• 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
• 16. Ercan, E. (2020) Conduct asset performance management with a software-based approach, Plant Engineering, 74(3), 27-29.
• 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
• 25. Kushwaha, N. L., Rajput, J., Suna, T., Sena, D. R., Singh, D. K., Mishra, A. K., Sharma, P. K. and Mani, I. (2023) Metaheuristic approaches for prediction of water quality indices with relief algorithm-based feature selection, Ecological Informatics, 75, 102122. doi:10.1016/j.ecoinf.2023.102122
• 26. Lai, C.-M., Chiu, C.-C., Shih, Y.-C. and Huang, H.-P. (2022) A hybrid feature selection algorithm using simplified swarm optimization for body fat prediction, Computer Methods and Programs in Biomedicine, 226, 107183. doi:10.1016/j.cmpb.2022.107183
• 27. Lap, B. Q., Phan, T.-T.-H., Nguyen, H. D., Quang, L. X., Hang, P. T., Phi, N. Q., Hoang, V. T., Linh, P. G. and Hang, B. T. T. (2023) Predicting Water Quality Index (WQI) by feature selection and machine learning: A case study of An Kim Hai irrigation system, Ecological Informatics, 74, 101991. doi:10.1016/j.ecoinf.2023. 101991
• 28. Li, W. and Becker, D. M. (2021) Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling, Energy, 237, 121543. doi:https://doi.org/10.1016/j.energy.2021.121543
• 29. Liu, W., Liu, T., Liu, Z., Luo, H. and Pei, H. (2023) A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction, Environmental Research, 224, 115560. doi:https://doi.org/10.1016/ j.envres.2023.115560
• 30. Liu, Y. and Schumann, M. (2005) Data mining feature selection for credit scoring models, Journal of the Operational Research Society, 56(9), 1099-1108. doi:10.1057/palgrave.jors.2601976
• 31. Marafino, B. J., John Boscardin, W. and Adams Dudley, R. (2015) Efficient and sparse feature selection for biomedical text classification via the elastic net: Application to ICU risk stratification from nursing notes, Journal of Biomedical Informatics, 54, 114-120. doi:10.1016/j.jbi.2015.02.003
• 32. Moldovan, D. and Slowik, A. (2021) Energy consumption prediction of appliances using machine learning and multi-objective binary grey wolf optimization for feature selection, Applied Soft Computing, 111, 107745. doi:10.1016/j.asoc.2021.107745
• 33. Naseri, H., Waygood, E. O. D., Wang, B., Patterson, Z. and Daziano, R. A. (2022) A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change, Sustainability, 14(1), 40. doi:10.3390/su14010040
• 34. Olu-Ajayi, R., Alaka, H., Sulaimon, I., Balogun, H., Wusu, G., Yusuf, W. and Adegoke, M. (2023) Building energy performance prediction: A reliability analysis and evaluation of feature selection methods, Expert Systems with Applications, 225, 120109. doi:10.1016/j.eswa.2023.120109
• 35. Pasha, S. J. and Mohamed, E. S. (2022) Advanced hybrid ensemble gain ratio feature selection model using machine learning for enhanced disease risk prediction, Informatics in Medicine Unlocked, 32, 101064. doi:10.1016/j.imu.2022.101064
• 36. Petersson, H., Klingvall, R. and Holmberg, M. (2009) Sensor array optimization using variable selection and a Scanning Light Pulse Technique, Sensors and Actuators B: Chemical, 142(2), 435-445. doi:10.1016/j.snb.2009.04.029
• 37. Piran, F. A. S., De Paris, A., Lacerda, D. P., Camargo, L. F. R., Serrano, R. and Cassel, R. A. (2020) Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis, Global Journal of Flexible Systems Management, 21(2), 191-206. doi:10.1007/s40171-020-00238-6
• 38. Prasetyo, Y. T. and Veroya, F. C. (2020). An Application of Overall Equipment Effectiveness (OEE) for Minimizing the Bottleneck Process in Semiconductor Industry. 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA).
• 39. Qiao, Q., Yunusa-Kaltungo, A. and Edwards, R. E. (2022) Feature selection strategy for machine learning methods in building energy consumption prediction, Energy Reports, 8, 13621-13654. doi:10.1016/j.egyr.2022.10.125
• 40. Qu, K., Xu, J., Hou, Q., Qu, K. and Sun, Y. (2023) Feature selection using Information Gain and decision information in neighborhood decision system, Applied Soft Computing, 136, 110100. doi:10.1016/j.asoc.2023.110100
• 41. Saud, S., Jamil, B., Upadhyay, Y. and Irshad, K. (2020) Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach, Sustainable Energy Technologies and Assessments, 40, 100768. doi:10.1016/j.seta.2020.100768
• 42. Sun, J., Liu, G., Sun, B. and Xiao, G. (2021) Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection, Applied Energy, 303, 117694. doi:10.1016/j.apenergy.2021.117694
• 43. Sun, S., Hu, M., Wang, S. and Zhang, C. (2023) How to capture tourists’ search behavior in tourism forecasts? A two-stage feature selection approach, Expert Systems with Applications, 213, 118895. doi:10.1016/j.eswa.2022.118895
• 44. Toğaçar, M., Ergen, B. and Cömert, Z. (2020a) Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders, Medical Hypotheses, 135, 109503. doi:10.1016/j.mehy.2019.109503
• 45. Toğaçar, M., Ergen, B. and Cömert, Z. (2020b) Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models, Measurement, 153, 107459. doi:10.1016/j.measurement.2019.107459
• 46. Tsanas, A. (2022) Relevance, redundancy, and complementarity trade-off (RRCT): A principled, generic, robust feature-selection tool, Patterns, 3(5), 100471. doi:10.1016/j.patter.2022.100471
• 47. Verma, D., Jansen, D., Bach, K., Poel, M., Mork, P. J. and d’Hollosy, W. O. N. (2022) Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes, BMC Medical Informatics and Decision Making, 22(1), 227. doi:10.1186/s12911-022-01973-9
• 48. Vogel-Heuser, B., Karaseva, V., Folmer, J. and Kirchen, I. (2017) Operator Knowledge Inclusion in Data-Mining Approaches for Product Quality Assurance using Cause-Effect Graphs, IFAC-PapersOnLine, 50(1), 1358-1365. doi:10.1016/j.ifacol.2017. 08.233
• 49. Wang, T., Hu, Z. and Liu, H. (2023) A unified view of feature selection based on Hilbert-Schmidt independence criterion, Chemometrics and Intelligent Laboratory Systems, 104807. doi:10.1016/j.chemolab.2023.104807
• 50. Wang, Z., Lei, Y., Cui, H., Miao, H., Zhang, D., Wu, Z. and Liu, G. (2022) Enhanced RBF neural network metamodelling approach assisted by sliced splitting-based K-fold cross-validation and its application for the stiffened cylindrical shells, Aerospace Science and Technology, 124, 107534. doi:10.1016/j.ast.2022.107534
• 51. Wei, Y., Chen, Z., Zhao, C., Chen, X., He, J. and Zhang, C. (2023) A time-varying ensemble model for ship motion prediction based on feature selection and clustering methods, Ocean Engineering, 270, 113659. doi:10.1016/j.oceaneng.2023.113659
• 52. Wu, T., Hao, Y., Yang, B. and Peng, L. (2023) ECM-EFS: An ensemble feature selection based on enhanced co-association matrix, Pattern Recognition, 139, 109449. doi:10.1016/j.patcog.2023.109449
• 53. Xing, W., Pei, B., Li, S., Chen, G. and Xie, C. (2019) Using learning analytics to support students’ engineering design: the angle of prediction, Interactive Learning Environments, 1-18. doi:10.1080/10494820.2019.1680391
• 54. Xu, G. and Wang, X. (2023) Support vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction, Measurement, 218, 113106. doi:10.1016/j.measurement.2023. 113106
• 55. Yan, W.-L. (2023) Stock index futures price prediction using feature selection and deep learning, The North American Journal of Economics and Finance, 64, 101867. doi:10.1016/j.najef.2022.101867
• 56. Yao, G., Hu, X. and Wang, G. (2022) A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain, Expert Systems with Applications, 200, 117002. doi:10.1016/j.eswa.2022.117002
• 57. Zhao, L., Li, Y., Li, S. and Ke, H. (2023) A frequency item mining based embedded feature selection algorithm and its application in energy consumption prediction of electric bus, Energy, 271, 126999. doi:10.1016/j.energy.2023.126999
• 58. Zhao, Z., Wu, J., Cai, F., Zhang, S. and Wang, Y.-G. (2022) A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting, Ecological Indicators, 144, 109416. doi:10.1016/j.ecolind.2022.109416
• 59. Zhou, J. and Zhang, W. (2023) Coal consumption prediction in thermal power units: A feature construction and selection method, Energy, 273, 126996. doi:10.1016/j.energy.2023.126996