Abstract
Classification which is used to predict the classes of objects has the disadvantage of ignoring the costs incurred in false predictions. However, wrong predictions can cause different degrees of costs. Therefore, cost-sensitive classification algorithms are in demand in order to improve quality of the classification. In this study, rule-based cost sensitive BEE-miner algorithm which was developed by making use of Bees Algorithm and MEPAR-miner algorithms are used to classify the defects in the production line of a textile company in a considerably better way. When results on the quality defect dataset are analysed, it is observed that BEE-miner algorithm outperforms the MEPAR-miner algorithm in terms of classification cost and accuracy.
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Frumosu FD, Khan AR, Schioler H, Kulahci M, Zaki M (2020) Cost-sensitive learning classification strategy for predicting product failures. Expert Syst Appl 161:113653
Koulali I, Eskil MT (2021) Unsupervised textile defect detection using convolutional neural networks. Appl Soft Comput 113:107913
Uzen H, Turkoglu M, Hanbay D (2021) Texture classification with multiple 8. Pooling and filter ensemble based on deep neural network. Expert Syst Appl 175:114838 (2021)
Jing JF, Ma H, Zhang HH (2019) Automatic fabric defect detection using deep convolutional neural networks. Color Technol 135:213–223
Özbakır L, Baykasoğlu A, Kulluk S (2011) Rule extraction from artificial neural networks to discover causes of quality defects in fabric production. Neural Comput Appl 20:1117–1128
Baykasoğlu A, Özbakır L, Kulluk S (2011) Classifying defect factors in fabric production via DIFACONN-miner: a case study. Expert Syst Appl 38:11321–11328
Pei W, Xue B, Shang L, Zhang M (2021) Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification. Appl Soft Comput 101:106989
Alotaibi R, Flach P (2021) Multi-label thresholding for cost-sensitive classification. Neurocomputing 436:232–247
Pham DT, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M (2006) The Bees algorithm—a novel tool for complex optimisation problems. Intell Prod Mach Syst 454–459
Pham DT, Pham QT, Ghanbarzadeh A, Castellani M (2008) Dynamic optimisation of chemical engineering processes using the Bees algorithm. IFAC Proc Vol 41:6100–6105
Ziarati K, Akbari R, Zeighami V (2011) On the performance of bee algorithms for resource-constrained project scheduling problem. Appl Soft Comput 11:3720–3733
Fahmy AA (2012) Using the Bees algorithm to select the optimal speed parameters for wind turbine generators. J King Saud Univ-Comput Inf Sci 24:17–26
Yüce B, Mastrocinque E, Lambiase A, Packanather MS, Pham DT (2014) A multi-objective supply chain optimisation using enhanced Bees Algorithm with adaptive neighbourhood search and site abandonment strategy. Swarm Evol Comput 18:71–82
Akpinar Ş, Baykasoğlu A (2014) Multiple colony bees algorithm for continuous spaces. Appl Soft Comput 24:829–841
Tsai HC (2014) Novel Bees algorithm: stochastic self-adaptive neighborhood. Appl Math Comput 247:1161–1172
Yüce B, Fruggiero F, Packianather MS, Pham DT, Mastrocinque E, Lambiase A, Fera M (2017) Hybrid genetic bees algorithm applied to single machine scheduling with earliness and tardiness penalties. Comput Ind Eng 113:842–858
Laili Y, Tao F, Pham DT, Wang Y, Zhang L (2019) Robotic disassembly re-planning using a two-pointer detection strategy and a super-fast Bees algorithm. Rob Comput Integr Manuf 59:130–142
Xu W, Tang Q, Liu J, Liu Z, Jhou Z, Pham DT (2020) Disassembly sequence planning using discrete Bees algorithm for human robot collaboration in remanufacturing. Rob Comput Integr Manuf 62:101860
Baronti L, Castellani M, Pham DT (2020) An analysis of the search mechanisms of the Bees algorithm. Swarm Evol Comput 59:100746
Tapkan P, Özbakır L, Kulluk S, Baykasoğlu A (2016) A cost-sensitive classification algorithm: BEE-Miner. Knowl-Based Syst 95:99–113
Kulluk S, Özbakır L, Tapkan PZ, Baykasoğlu A (2016) Cost-sensitive meta-learning classifiers: MEPAR-miner and DIFACONN-miner. Knowl-Based Syst 98:148–161
Oltean M, Dumitrescu D (2021) Multi expression programming. Technical Note, Department of Computer Science, Babes-Bolyai University, RO
Weiss Y, Elovici Y, Rokach L (2013) The CASH algorithm-cost-sensitive attribute selection using histograms. Inf Sci 222:247–268
Pietraszek T (2006) Alert classification to reduce false positives in intrusion detection. PhD thesis, Computer Science, University of Freiburg, DE
Baykasoğlu A, Özbakır L (2007) MEPAR-miner: multi-expression programming for classification rule mining. Eur J Oper Res 183:767–784
Yang Y, Webb GI (2009) Discretization for Naive-Bayes learning: managing discretization bias and variance. Mach Learn 74:39–74
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Ay, M., Baykasoglu, A., Ozbakir, L., Kulluk, S. (2023). A Case Study with the BEE-Miner Algorithm: Defects on the Production Line. In: Pham, D.T., Hartono, N. (eds) Intelligent Production and Manufacturing Optimisation—The Bees Algorithm Approach. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-031-14537-7_4
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DOI: https://doi.org/10.1007/978-3-031-14537-7_4
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