Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles

International Journal of Computer Integrated Manufacturing

Volumn 31, Issue 8, 2018, Pages 701-717

  Kim, Aekyung ^a   Oh, Kyuhyup ^a   Jung, Jae Yoon ^a   Kim, Bohyun ^b  

^a Kyung Hee University   (South Korea)

^b Korea Institute of Industrial Technology   (South Korea)

Author keywords

cost sensitive ensemble classification; decision tree ensemble; die casting quality analysis; Imbalanced classification; manufacturing quality condition classification

Indexed keywords

ADVANCED ANALYTICS; DATA ANALYTICS; DECISION TREES; DEFECTS; DIE CASTING; QUALITY CONTROL; TREES (MATHEMATICS);

ADVANCED MANUFACTURING; C4.5 DECISION TREES; CASTING QUALITY; CLASSIFICATION ALGORITHM; ENSEMBLE CLASSIFICATION; IMBALANCED CLASSIFICATION; MANUFACTURING PROCESS; MANUFACTURING QUALITY;

COST BENEFIT ANALYSIS;

EID: 85035764582     PISSN: 0951192X     EISSN: 13623052     Source Type: Journal    
DOI: 10.1080/0951192X.2017.1407447     Document Type: Article

References (60)

1. Aghabozorgi, S., A. S., Shirkhorshidi, and T. Y., Wah. 2015. “Time-Series Clustering - A Decade Review.” Information Systems 53: 16–38. doi:10.1016/j.is.2015.04.007.
2. Akıncıoğlu, S., F., Mendi, A., Çiçek, and G., Akıncıoğlu. 2013. “ANN-based Prediction of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel.” International Journal of Advanced Manufacturing Technology 68 (1–4): 197–207. doi:10.1007/s00170-012-4719-6.
3. Asiltürk, İ., M., Tinkir, H., El Monuayri, and L., Çelik. 2012. “An Intelligent System Approach for Surface Roughness and Vibrations Prediction in Cylindrical Grinding.” International Journal of Computer Integrated Manufacturing 25 (8): 750–759. doi:10.1080/0951192X.2012.665185.
4. Bagnall, A., E., Keogh, S., Lonardi, and G., Janacek. 2006. “A Bit Level Representation for Time-Series Data Mining with Shape Based Similarity.” Data Mining and Knowledge Discovery 13: 11–40. doi:10.1007/s10618-005-0028-0.
5. Barandela, R., R. M., Valdovinos, and J. S., Sánchez. 2003. “New Applications of Ensembles of Classifiers.” Pattern Analysis & Applications 6 (3): 245–256. doi:10.1007/s10044-003-0192-z.
6. Binu Bose, V., and K. N., Anilkuma. 2013. “Reducing Rejection Rate of Castings Using Simulation Model.” International Journal of Innovative Research in Science, Engineering and Technology 2: 589–597.
7. Bradley, A. P., 1997. “The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms.” Pattern Recognition 30 (7): 1145–1159. doi:10.1016/S0031-3203(96)00142-2.
8. Breiman, L., 1996. “Bagging Predictors." Machine Learning 24 (2): 123–140.
9. Breiman, L., 1998. “Arcing Classifier (With Discussion and a Rejoinder by the Author).” The Annals of Statistics 26 (3): 801–849. doi:10.1214/aos/1024691079.
10. Breiman, L., 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
11. Breiman, L., J., Friedman, C. J., Stone, and R. A., Olshen. 1984. Classification and Regression Trees. NY: Chapman and Hall/CRC press.
12. Chawla, N. V., N., Japkowicz, and A., Kotcz. 2004. “Special Issue on Learning from Imbalanced Data Sets.” ACM SIGKDD Explorations Newsletter 6 (1): 1–6. doi:10.1145/1007730.1007733.
13. Chen, W. C., P. H., Tai, M. W., Wang, W. J., Deng, and C. T., Chen. 2008. “A Neural Network-Based Approach for Dynamic Quality Prediction in A Plastic Injection Molding Process.” Expert Systems with Applications 35 (3): 843–849. doi:10.1016/j.eswa.2007.07.037.
14. Chiang, K. T., N. M., Liu, and C. C., Chou. 2008. “Machining Parameters Optimization on the Die Casting Process of Magnesium Alloy Using the Grey-Based Fuzzy Algorithm.” International Journal of Advanced Manufacturing Technology 38 (3–4): 229–237. doi:10.1007/s00170-007-1103-z.
15. Chien, C. F., S. C., Hsu, and Y. J., Chen. 2013. “A System for Online Detection and Classification of Wafer Bin Map Defect Patterns for Manufacturing Intelligence.” International Journal of Production Research 51 (8): 2324–2338. doi:10.1080/00207543.2012.737943.
16. Cho, H. W., 2015. “Enhanced Real-Time Quality Prediction Model Based on Feature Selected Nonlinear Calibration Techniques.” International Journal of Advanced Manufacturing Technology 78 (1–4): 633–640. doi:10.1007/s00170-014-6664-z.
17. Chou, P. H., M. J., Wu, and K. K., Chen. 2010. “Integrating Support Vector Machine and Genetic Algorithm to Implement Dynamic Wafer Quality Prediction System.” Expert Systems with Applications 37 (6): 4413–4424. doi:10.1016/j.eswa.2009.11.087.
18. Cortes, C., and V., Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–297. doi:10.1007/BF00994018.
19. Das, N., 2008. “Reducing Manufacturing Defect through Statistical Investigation in an Integrated Aluminium Industry.” International Journal of Advanced Manufacturing Technology 36 (3–4): 315–321. doi:10.1007/s00170-006-0850-6.
20. Er, M. J., J., Liao, and J., Lin. 2000. “Fuzzy Neural Networks-Based Quality Prediction System for Sintering Process.” IEEE Transactions on Fuzzy Systems 8 (3): 314–324. doi:10.1109/91.855919.
21. Fan, R. E., K. W., Chang, C. J., Hsieh, X. R., Wang, and C. J., Lin. 2008. “LIBLINEAR: A Library for Large Linear Classification.” Journal of Machine Learning Research 9: 1871–1874.
22. Freund, Y., and R. E., Schapire. 1995. “A Desicion-Theoretic Generalization of On-Line Learning and an Application to Boosting.” In Proceedings of the European Conference on Computational Learning Theory. Springer, pp. 23–37.
23. Fu, M. W., and M. S., Yong. 2009. “Simulation-Enabled Casting Product Defect Prediction in Die Casting Process.” International Journal of Production Research 47 (18): 5203–5216. doi:10.1080/00207540801935616.
24. Galar, M., A., Fernandez, E., Barrenechea, H., Bustince, and F., Herrera. 2012. “A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches.” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42 (4): 463–484. doi:10.1109/TSMCC.2011.2161285.
25. Ge, Z., T., Chen, and Z., Song. 2011. “Quality Prediction for Polypropylene Production Process Based on CLGPR Model.” Control Engineering Practice 19 (5): 423–432. doi:10.1016/j.conengprac.2011.01.002.
26. Haibo, H., and E. A., Garcia. 2009. “Learning from Imbalanced Data.” IEEE Transactions on Knowledge and Data Engineering 21 (9): 1263–1284. doi:10.1109/TKDE.2008.239.
27. Heidarzadeh, A., M., Jabbari, and M., Esmaily. 2015. “Prediction of Grain Size and Mechanical Properties in Friction Stir Welded Pure Copper Joints Using a Thermal Model.” International Journal of Advanced Manufacturing Technology 77 (9–12): 1819–1829. doi:10.1007/s00170-014-6543-7.
28. Holst, A., 2008. “Defect Prediction in Hot Strip Rolling Using ANN and SVM.” In Proceedings of the Tenth Scandinavian Conference on Artificial Intelligence (SCAI 2008) 173: 44. IOS Press.
29. Hsu, Q. C., and A. T., Do. 2013. “Minimum Porosity Formation in Pressure Die Casting by Taguchi Method.” Mathematical Problems in Engineering, 2013.
30. Japkowicz, N., and S., Stephen. 2002. “The Class Imbalance Problem: A Systematic Study.” Intelligent Data Analysis 6 (5): 429–449.
31. Kagermann, H., W. D., Lukas, and W., Wahlster. 2011. Industrie 4.0: Mit Dem Internet Der Dinge Auf Dem Weg Zur 4. Industriellen Revolution, 13, 11. VDI nachrichten, Berlin.
32. Karunakar, D. B., and G. L., Datta. 2008. “Prevention of Defects in Castings Using Back Propagation Neural Networks.” International Journal of Advanced Manufacturing Technology 39 (11–12): 1111–1124. doi:10.1007/s00170-007-1289-0.
33. Kim, I. S., Y. J., Jeong, C. W., Lee, and P. K., Yarlagadda. 2003. “Prediction of Welding Parameters for Pipeline Welding Using an Intelligent System.” International Journal of Advanced Manufacturing Technology 22 (9–10): 713–719. doi:10.1007/s00170-003-1589-y.
34. Krimpenis, A., Y. J., Jeong, C. W., Lee, and P. K. D. V., Yarlagadda. 2006. “Simulation-Based Selection of Optimum Pressure Die-Casting Process Parameters Using Neural Nets and Genetic Algorithms.” International Journal of Advanced Manufacturing Technology 27 (5–6): 509–517. doi:10.1007/s00170-004-2218-0.
35. Li, E., L., Jia, and J., Yu. 2004. “A Genetic Neural Fuzzy System and Its Application in Quality Prediction in the Injection Process.” Chemical Engineering Communications 191 (3): 335–355. doi:10.1080/00986440490272537.
36. Liu, X. Y., J., Wu, and Z. H., Zhou. 2009. “Exploratory Undersampling for Class-Imbalance Learning.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39 (2): 539–550. doi:10.1109/TSMCB.2008.2007853.
37. Lueth, K. L., 2016. “Will the Industrial Internet Disrupt the Smart Factory of the Future?” IoT Analytics (accessed February29, 2016). http://iot-analytics.com/industrial-internet-disrupt-smart-factory
38. Mathivanan, D., and N. S., Parthasarathy. 2009. “Prediction of Sink Depths Using Nonlinear Modeling of Injection Molding Variables.” International Journal of Advanced Manufacturing Technology 43 (7–8): 654–663. doi:10.1007/s00170-008-1749-1.
39. Mohanty, C., and B. K., Jena. 2014. “Optimization of Aluminum Die Casting Process Using Artificial Neural Network.” International Journal of Emerging Technology and Advanced Engineering 4 (7): 146-149.
40. Pal, S., S. K., Pal, and A. K., Samantaray. 2010. “Prediction of the Quality of Pulsed Metal Inert Gas Welding Using Statistical Parameters of Arc Signals in Artificial Neural Network.” International Journal of Computer Integrated Manufacturing 23 (5): 453–465. doi:10.1080/09511921003667698.
41. Quinlan, J. R., 1996. “Improved Use of Continuous Attributes in C4.5.” Journal of Artificial Intelligence Research 4: 77–90.
42. Ripley, B. D., 2007. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
43. Rokach, L., and O., Maimon. 2006. “Data Mining for Improving the Quality of Manufacturing: A Feature Set Decomposition Approach.” Journal of Intelligent Manufacturing 17 (3): 285–299. doi:10.1007/s10845-005-0005-x.
44. Samanta, B., 2009. “Surface Roughness Prediction in Machining Using Soft Computing.” International Journal of Computer Integrated Manufacturing 22 (3): 257–266. doi:10.1080/09511920802287138.
45. Santos, I., J., Nieves, P., Bringas, and Y., Penya. 2010. “Machine-Learning-Based Defect Prediction in High Precision Foundry Production.” In Structural Steel and Castings: Shapes and Standards, Properties and Applications, edited by Becker, L. M., Hauppauge: Nova Science Publishers, 259–276.
46. Sata, A., and B., Ravi. 2017. “Bayesian Inference-Based Investment-Casting Defect Analysis System for Industrial Application.” International Journal of Advanced Manufacturing Technology 90 (9-12): 3301-3315.
47. Savalina, I., K., Sabo, and G., Šimunović. 2011. “Machined Surface Quality Prediction Models Based on Moving Least Squares and Moving Least Absolute Deviations Methods.” International Journal of Advanced Manufacturing Technology 57 (9–12): 1099–1106. doi:10.1007/s00170-011-3353-z.
48. Schapire, R. E., and Y., Singer. 1999. “Improved Boosting Algorithms Using Confidence-Rated Predictions.” Machine Learning 37 (3): 297–336. doi:10.1023/A:1007614523901.
49. Seiffert, C., T. M., Khoshgoftaar, J., Van Hulse, and A., Napolitano. 2010. “RUSBoost: A Hybrid Approach to Alleviating Class Imbalance.” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 40 (1): 185–197. doi:10.1109/TSMCA.2009.2029559.
50. Shen, C., L., Wang, and Q., Li. 2007. “Optimization of Injection Molding Process Parameters Using Combination of Artificial Neural Network and Genetic Algorithm Method.” Journal of Materials Processing Technology 183 (2): 412–418. doi:10.1016/j.jmatprotec.2006.10.036.
51. Singaram, L., 2010. “Improving Quality of Sand Casting Using Taguchi Method and ANN Analysis.” International Journal on Design and Manufacturing Technologies 4 (1): 1–5. doi:10.18000/ijodam.70071.
52. Staelin, C., 2003. “Parameter Selection for Support Vector Machines.” HP Laboratorie Israel, Technical Reports HPL-2002-354R1.
53. Sun, Y., M. S., Kamel, A. K. C., Wong, and Y., Wang. 2007. “Cost-Sensitive Boosting for Classification of Imbalanced Data.” Pattern Recognition 40: 3358–3378. doi:10.1016/j.patcog.2007.04.009.
54. Suresh, P. V. S., P. V., Rao, and S. G., Deshmukh. 2002. “A Genetic Algorithmic Approach for Optimization of Surface Roughness Prediction Model.” International Journal of Machine Tools and Manufacture 42 (6): 675–680. doi:10.1016/S0890-6955(02)00005-6.
55. Syrcos, G. P., 2003. “Die Casting Process Optimization Using Taguchi Methods.” Journal of Materials Processing Technology 135 (1): 68–74. doi:10.1016/S0924-0136(02)01036-1.
56. Wang, D., 2011. “Robust Data-Driven Modeling Approach for Real-Time Final Product Quality Prediction in Batch Process Operation.” IEEE Transactions on Industrial Informatics 7 (2): 371–377. doi:10.1109/TII.2010.2103401.
57. Wang, S., and X., Yao. 2009(March). “Diversity Analysis on Imbalanced Data Sets by Using Ensemble Models.” In Proceedings of IEEE Symposium on Computational Intelligence and Data Mining 2009 (CIDM’09), pp. 324–331.
58. Yarlagadda, P. K., 2000. “Prediction of Die Casting Process Parameters by Using an Artificial Neural Network Model for Zinc Alloys.” International Journal of Production Research 38 (1): 119–139. doi:10.1080/002075400189617.
59. Zhang, G., J., Li, Y., Chen, Y., Huang, X., Shao, and M., Li. 2014. “Prediction of Surface Roughness in End Face Milling Based on Gaussian Process Regression and Cause Analysis considering Tool Vibration.” International Journal of Advanced Manufacturing Technology 75 (9–12): 1357–1370. doi:10.1007/s00170-014-6232-6.
60. Zhang, L., and R., Wang. 2013. “An Intelligent System for Low-Pressure Die-Cast Process Parameters Optimization.” International Journal of Advanced Manufacturing Technology 65 (1–4): 517–524. doi:10.1007/s00170-012-4190-4.