Abstract
In this study, a soft computing-based approach DIFACONN-miner II algorithm which simultaneously selects features, trains Artificial Neural Networks (ANNs) and extract classification rules is presented to classify the quality defect factors in a major textile company in Turkey. DIFACONN-miner II algorithm has a three-layered nested structure. In the outer layer of the algorithm feature selection task is done by Genetic Algorithm (GA), in the middle layer ANNs are trained by Differential Evolution (DE) algorithm and in the inner layer classification rules are extracted from trained ANNs by Touring Ant Colony Optimization (TACO) algorithm. The fitness function of the DIFACONN-miner II algorithm has a multi-objective structure including accuracy, number of features, number of rules and error of artificial neural networks. The main motivation behind this study is to determine the causes of quality defects and prevent their occurrence. The features and their values that give the effective results are tried to be discovered and evaluated by using the DIFACONN-miner II algorithm. The preliminary results show that the DIFACONN-miner II algorithm is able to produce accurate and comprehensible classification rules to identify the effective factors on quality defects.
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References
Jothishankar, M.C., Wu, T., Roberts, J., Shiau, J.-Y.: Case study: applying data mining to defect diagnosis. J. Adv. Manuf. Syst. 3(1), 69–83 (2004)
Baykasoglu, A., Ozbakır, L.: MEPAR-miner: multi-expression programming for classification rule mining. Eur. J. Oper. Res. 183(2), 767–784 (2007)
Ozbakır, L., Baykasoglu, A., Kulluk, S.: A soft computing-based approach for integrated training and rule extraction fromartificial neural networks: DIFACONN-miner. Appl. Soft. Comput. 10(1), 304–317 (2010)
Guyon, I., Elisseeff, A.: Introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Nag, K., Pal, N.R.: A multiobjective genetic programming based ensemble for simultaneous feature selection and classification. J. Latex Cl. Files 11(4), 1–12 (2012)
Chen, Z., Li, J.: A multiple kernel support vector machine scheme for simultaneous feature selection and rule-based classification. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007, LNAI, vol. 4426, pp. 441–448. Springer-Verlag, Berlin and Heidelberg (2007)
Zou, H.: An improved 1-norm SVM for simultaneous classification and variable selection. AISTATS 2007 (2007)
Gurav, A., Nair, V., Gupta, U., Valadi, J.: Glowworm swarm based informative attribute selection using support vector machines for simultaneous feature selection and classification. In: Panigrahi, B.K., et al. (eds.) SEMCCO 2014, LNCS, vol. 8947, pp. 27–37. Springer International Publishing, Cham, Switzerland (2015)
Maldonado, S., Weber, R., Basak, J.: Simultaneous feature selection and classification using kernel-penalized support vector machines. Inf. Sci. 181, 115–128 (2011)
Aljarah, I., Al-Zoubi, A.M., Faris, H., Hassonah, M.A., Mirjalili, S., Saadeh, H.: Simultaneous feature selection and support vector machine optimization using grasshopper optimization algorithm. Cogn. Comput. 10, 478–495 (2018)
Kumar, D.S., Rao, V.M.: Simultaneous feature selection and classification using fuzzy rules. In: Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018), pp. 125–130. IEEE Xplore Compliant - Part Number: CFP18BAC-ART (2018). ISBN:978-1-5386-1974-2
Chakraborty, D., Pal, N.R.: A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification. IEEE Trans. Neural Netw. 15(1), 110–123 (2004)
Su, C.-T., Hsiao, Y.-H.: Multiclass MTS for simultaneous feature selection and classification. IEEE Trans. Knowl. Data Eng. 21(2), 192–205 (2009)
Dunbar, M., Murray, J.M., Cysique, L.A., Brew, B.J., Jeyakumar, V.: Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment. Eur. J. Oper. Res. 206, 470–478 (2010)
Ozbakır, L., Baykasoglu, A., Kulluk, S., Yapıcı, H.: TACO-miner: an ant colony based algorithm for rule extraction from trained neural networks. Expert Syst. Appl. 36(10), 12295–12305 (2009)
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Kulluk, S., Özbakır, L. (2020). DIFACONN-Miner II Algorithm to Discover Causes of Quality Defects. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_136
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DOI: https://doi.org/10.1007/978-3-030-23756-1_136
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