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

Ümit Yılmaz; Özlem Kuvat

doi:10.17482/uumfd.1296479

Araştırma Makalesi

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: 1

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

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

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

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

Ümit Yılmaz Özlem Kuvat

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

Cited By: 1

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

Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Endüstri Mühendisliği
Bölüm	Araştırma Makaleleri
Yazarlar	Ümit Yılmaz 0000-0003-4268-8598 Özlem Kuvat 0000-0001-7017-4557
Erken Görünüm Tarihi	18 Ağustos 2023
Yayımlanma Tarihi	31 Ağustos 2023
Gönderilme Tarihi	12 Mayıs 2023
Kabul Tarihi	13 Temmuz 2023
Yayımlandığı Sayı	Yıl 2023 Cilt: 28 Sayı: 2

Kaynak Göster

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. Ağustos 2023;28(2):437-452. doi:10.17482/uumfd.1296479
Chicago	Yılmaz, Ümit, ve Özlem Kuvat. “INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 28, sy. 2 (Ağustos 2023): 437-52. https://doi.org/10.17482/uumfd.1296479.
EndNote	Yılmaz Ü, Kuvat Ö (01 Ağustos 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 ve Ö. Kuvat, “INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION”, UUJFE, c. 28, sy. 2, ss. 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 (Ağustos 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 ve Özlem Kuvat. “INVESTIGATING THE EFFECT OF FEATURE SELECTION METHODS ON THE SUCCESS OF OVERALL EQUIPMENT EFFECTIVENESS PREDICTION”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, c. 28, sy. 2, 2023, ss. 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.

Cited By

Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data

Applied Sciences

https://doi.org/10.3390/app132011127

Makale Dosyaları

Tam Metin

DUYURU:

30.03.2021- Nisan 2021 (26/1) sayımızdan itibaren TR-Dizin yeni kuralları gereği, dergimizde basılacak makalelerde, ilk gönderim aşamasında Telif Hakkı Formu yanısıra, Çıkar Çatışması Bildirim Formu ve Yazar Katkısı Bildirim Formu da tüm yazarlarca imzalanarak gönderilmelidir. Yayınlanacak makalelerde de makale metni içinde "Çıkar Çatışması" ve "Yazar Katkısı" bölümleri yer alacaktır. İlk gönderim aşamasında doldurulması gereken yeni formlara "Yazım Kuralları" ve "Makale Gönderim Süreci" sayfalarımızdan ulaşılabilir. (Değerlendirme süreci bu tarihten önce tamamlanıp basımı bekleyen makalelerin yanısıra değerlendirme süreci devam eden makaleler için, yazarlar tarafından ilgili formlar doldurularak sisteme yüklenmelidir). Makale şablonları da, bu değişiklik doğrultusunda güncellenmiştir. Tüm yazarlarımıza önemle duyurulur.

Bursa Uludağ Üniversitesi, Mühendislik Fakültesi Dekanlığı, Görükle Kampüsü, Nilüfer, 16059 Bursa. Tel: (224) 294 1907, Faks: (224) 294 1903, e-posta: mmfd@uludag.edu.tr