{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:53:55Z","timestamp":1780638835642,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,18]],"date-time":"2021-04-18T00:00:00Z","timestamp":1618704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree\u2019s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.<\/jats:p>","DOI":"10.3390\/s21082849","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T21:59:49Z","timestamp":1618869589000},"page":"2849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing"],"prefix":"10.3390","volume":"21","author":[{"given":"Sungbum","family":"Jun","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kapteyn, M.G., Knezevic, D.J., and Willcox, K. (2020). Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. Proceedings of the AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics.","DOI":"10.2514\/6.2020-0418"},{"key":"ref_2","first-page":"151","article-title":"Wafer fault detection and key step identification for semiconductor manufacturing using principal component analysis, AdaBoost and decision tree","volume":"33","author":"Fan","year":"2016","journal-title":"J. Ind. Prod. Eng."},{"key":"ref_3","first-page":"23","article-title":"Machine learning in manufacturing: advantages, challenges, and applications","volume":"4","author":"Wuest","year":"2016","journal-title":"Prod. Manuf. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s10845-008-0148-7","article-title":"Optimizing a batch manufacturing process through interpretable data mining models","volume":"20","author":"Last","year":"2009","journal-title":"J. Intell. Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3389\/frai.2020.00003","article-title":"Interpretability With Accurate Small Models","volume":"3","author":"Ghose","year":"2020","journal-title":"Front. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mapa, J.S., Sison, A., and Medina, R.P. (2019, January 20\u201321). A Modified C4.5 Classification Algorithm: With the Discretization Method in Calculating the Goodness Score Equivalent. Proceedings of the 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICETAS48360.2019.9117309"},{"key":"ref_7","first-page":"25","article-title":"Increasing Accuracy of C4. 5 Algorithm by Applying Discretization and Correlation-based Feature Selection for Chronic Kidney Disease Diagnosis","volume":"12","author":"Cahyani","year":"2020","journal-title":"J. Telecommun. Electron. Comput. Eng. (JTEC)"},{"key":"ref_8","first-page":"29","article-title":"Comparative analysis of supervised and unsupervised discretization techniques","volume":"2","author":"Dash","year":"2011","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ram\u00edrez-Gallego, S., Garc\u00eda, S., Ben\u00edtez, J.M., and Herrera, F. (2016). A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees. Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, Springer.","DOI":"10.1007\/978-3-319-26227-7_5"},{"key":"ref_10","unstructured":"Kaya, F. (2008). Discretizing Continuous Features for Na\u00efve Bayes and C4. 5 Classifiers, University of Maryland Publications."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/TSM.2017.2676245","article-title":"A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes","volume":"30","author":"Lee","year":"2017","journal-title":"Ieee Trans. Semicond. Manuf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.eswa.2017.11.045","article-title":"Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data","volume":"95","author":"Ragab","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3172","DOI":"10.1109\/JSEN.2019.2958787","article-title":"Interpretable convolutional neural network through layer-wise relevance propagation for machine fault diagnosis","volume":"20","author":"Grezmak","year":"2019","journal-title":"Ieee Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hansen, L.K., and Rieger, L. (2019). Interpretability in intelligent systems\u2013a new concept?. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer.","DOI":"10.1007\/978-3-030-28954-6_3"},{"key":"ref_15","unstructured":"Quinlan, J.R. Unknown attribute values in induction. Proceedings of the Sixth International Workshop on Machine Learning."},{"key":"ref_16","unstructured":"Quinlan, J.R. (1993). C4. 5: Programs for Machine Learning, Morgan Kaufmann."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1162\/EVCO_a_00101","article-title":"Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms","volume":"21","author":"Barros","year":"2013","journal-title":"Evol. Comput."},{"key":"ref_18","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, CRC Press."},{"key":"ref_19","first-page":"97","article-title":"Comparative study ID3, cart and C4. 5 decision tree algorithm: A survey","volume":"27","author":"Singh","year":"2014","journal-title":"Int. J. Adv. Inf. Sci. Technol. (IJAIST)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1016\/j.asr.2007.07.020","article-title":"Comparison of decision tree methods for finding active objects","volume":"41","author":"Zhao","year":"2008","journal-title":"Adv. Space Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2008","journal-title":"Mach. Learn."},{"key":"ref_23","unstructured":"Freund, Y., and Schapire, R.E. (July, January 28). Game theory, on-line prediction and boosting. Proceedings of the Ninth Annual Conference on Computational Learning Theory, Desenzano del Garda, Italy."},{"key":"ref_24","first-page":"987","article-title":"Modest AdaBoost-teaching AdaBoost to generalize better","volume":"12","author":"Vezhnevets","year":"2005","journal-title":"Graphicon"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agrformet.2018.08.019","article-title":"Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China","volume":"263","author":"Fan","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1023\/A:1022876330390","article-title":"The limitations of decision trees and automatic learning in real world medical decision making","volume":"21","author":"Zorman","year":"1997","journal-title":"J. Med Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lakkaraju, H., Bach, S.H., and Leskovec, J. (2016, January 13\u201317). Interpretable decision sets: A joint framework for description and prediction. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939874"},{"key":"ref_29","first-page":"1","article-title":"Learning Certifiably Optimal Rule Lists for Categorical Data","volume":"18","author":"Angelino","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1023\/A:1016304305535","article-title":"Discretization: An Enabling Technique","volume":"6","author":"Liu","year":"2002","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2012.35","article-title":"A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning","volume":"25","author":"Garcia","year":"2012","journal-title":"Ieee Trans. Knowl. Data Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kwedlo, W., and Kr\u0119towski, M. (1999, January 15\u201318). An evolutionary algorithm using multivariate discretization for decision rule induction. Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-48247-5_48"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dougherty, J., Kohavi, R., and Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. Machine Learning Proceedings 1995, Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-377-6.50032-3"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1613\/jair.279","article-title":"Improved use of continuous attributes in C4.5","volume":"4","author":"Quinlan","year":"1996","journal-title":"J. Artif. Intell. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.ins.2019.07.091","article-title":"The optimal combination of feature selection and data discretization: An empirical study","volume":"505","author":"Tsai","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.1109\/CEC.2003.1299913","article-title":"An evolution strategies approach to the simultaneous discretization of numeric attributes in data mining","volume":"Volume 3","author":"Valdes","year":"2003","journal-title":"Proceedings of the 2003 Congress on Evolutionary Computation, 2003. CEC\u201903"},{"key":"ref_37","first-page":"595","article-title":"Multivariate discretization based on evolutionary cut points selection for classification","volume":"46","author":"Herrera","year":"2015","journal-title":"Ieee Trans. Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zamudio-Reyes, R., Cruz-Ram\u00edrez, N., and Mezura-Montes, E. (2017, January 14\u201316). A multivariate discretization algorithm based on multiobjective optimization. Proceedings of the 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI.2017.62"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s00500-016-2475-5","article-title":"MEMOD: A novel multivariate evolutionary multi-objective discretization","volume":"22","author":"Tahan","year":"2018","journal-title":"Soft Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1136\/amiajnl-2012-000929","article-title":"Discretization of continuous features in clinical datasets","volume":"20","author":"Maslove","year":"2013","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. European Working Session on Learning, Springer.","DOI":"10.1007\/BFb0017012"},{"key":"ref_42","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"1","author":"MacQueen","year":"1967","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.spl.2010.01.015","article-title":"A clustering-based discretization for supervised learning","volume":"80","author":"Gupta","year":"2010","journal-title":"Stat. Probab. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-12-309","article-title":"Application of an efficient Bayesian discretization method to biomedical data","volume":"12","author":"Lustgarten","year":"2011","journal-title":"BMC Bioinform."},{"key":"ref_45","unstructured":"Vannucci, M., and Colla, V. (2004, January 28\u201330). Meaningful discretization of continuous features for association rules mining by means of a SOM. Proceedings of the ESANN, Bruges, Belgium."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1162\/evco.1996.4.4.361","article-title":"A comparison of selection schemes used in evolutionary algorithms","volume":"4","author":"Blickle","year":"1996","journal-title":"Evol. Comput."},{"key":"ref_47","unstructured":"Ministry of SMEs and Startups of Korea & Korea AI Manufacturing Platform (KAMP) (2021, March 15). CNC Machine and Pasteurizer AI Datasets. Available online: https:\/\/kamp-ai.kr\/front\/dataset."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-020-01134-w","article-title":"MINDWALC: Mining interpretable, discriminative walks for classification of nodes in a knowledge graph","volume":"20","author":"Vandewiele","year":"2020","journal-title":"Bmc Med Inform. Decis. Mak."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"62762","DOI":"10.1109\/ACCESS.2020.2985255","article-title":"A new splitting criterion for better interpretable trees","volume":"8","author":"Hwang","year":"2020","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:26Z","timestamp":1760161766000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,18]]},"references-count":49,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21082849"],"URL":"https:\/\/doi.org\/10.3390\/s21082849","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,18]]}}}