Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2019, Cilt: 7 Sayı: 3, 576 - 590, 27.09.2019

Duygu Yılmaz Eroğlu

https://doi.org/10.29109/gujsc.549890

Öz

Kaynakça

  • [1] HILLAKER, H.: John Boyd, USAF Retired, Father of the F16, Code one magazine, July 1997.
  • [2] Galinec, D., & Steingartner, W. (2013). A Look at Observe, Orient, Decide and Act Feedback Loop, Pattern-Based Strategy and Network Enabled Capability for Organizations Adapting to Change. Acta Electrotechnica et Informatica, 13(2), 39.
  • [3] W. Ni , A Review and Comparative Study on Univariate Feature Selection Techniques, University of Cincinnati, 2012 Ph.D. thesis .
  • [4] Y. Saeys , I. Inza , P. Larrañaga , A review of feature selection techniques in bioinformatics, Bioinformatics 23 (19) (2007) 2507–2517 .
  • [5] J.R. Vergara , P.A. Estévez , A review of feature selection methods based on mutual information, Neural Comput. Appl. 24 (1) (2014) 175–186 .
  • [6] M. Seera , C.P. Lim , A hybrid intelligent system for medical data classification, Expert Syst. Appl. 41 (5) (2014) 2239–2249 .
  • [7] K. Bache, M. Lichman, UCI Machine Learning Repository, 2013 http://archive.ics.uci.edu/ml .
  • [8] Y. Marinakis , M. Marinaki , A hybridized particle swarm optimization with expanding neighborhood topology for the feature selection problem, in: Hybrid Metaheuristics, Springer, 2013, pp. 37–51 .
  • [9] S. Chatterjee , A. Bhattacherjee , Genetic algorithms for feature selection of image analysis-based quality monitoring model: an application to an iron mine, Eng. Appl. Artif. Intell. 24 (5) (2011) 786–795.
  • [10] C.-F. Tsai , W. Eberle , C.-Y. Chu , Genetic algorithms in feature and instance selection, Knowl. Based. Syst. 39 (2013) 240–247 .
  • [11] N. Das , R. Sarkar , S. Basu , M. Kundu , M. Nasipuri , D.K. Basu , A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application, Appl. Soft. Comput. 12 (5) (2012) 1592–1606 .
  • [12] H. Du., Data Mining Techniques and Applications: an introduction, Cengage Learning EMEA (2010).
  • [13] Sousa, T., Silva, A., & Neves, A. (2004). Particle swarm based data mining algorithms for classification tasks. Parallel computing, 30(5-6), 767-783.
  • [14] M. Zhao , C. Fu , L. Ji , K. Tang , M. Zhou , Feature selection and parameter optimization for support vector machines: a new approach based on genetic algorithm with feature chromosomes, Expert Syst. Appl. 38 (5) (2011) 5197–5204.
  • [15] S. Li , H. Wu , D. Wan , J. Zhu , An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine, Knowl. Based Syst. 24 (1) (2011) 40–48.
  • [16] A . Özçift , A . Gülten , Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases, Digital Signal Process. 23 (1) (2013) 230–237.
  • [17] Haykin, S. S., Haykin, S. S., Haykin, S. S., & Haykin, S. S. (2009). Neural networks and learning machines (Vol. 3). Upper Saddle River: Pearson.
  • [18] Fu, X., & Wang, L. (2003). Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33(3), 399-409.
  • [19] Janik, P., & Lobos, T. (2006). Automated classification of power-quality disturbances using SVM and RBF networks. IEEE Transactions on Power Delivery, 21(3), 1663-1669.
  • [20] Yildirim, P., Birant, D., & Alpyildiz, T. (2018). Data mining and machine learning in textile industry. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1228.
  • [21] Akyol, U., Tüfekci, P., Kahveci, K., & Cihan, A. (2015). A model for predicting drying time period of wool yarn bobbins using computational intelligence techniques. Textile Research Journal, 85(13), 1367-1380.
  • [22] Yildiz, Z., Dal, V., Ünal, M., & Yildiz, K. (2013). Use of artificial neural networks for modelling of seam strength and elongation at break. Fibres & Textiles in Eastern Europe.
  • [23] Behera, B. K., & Karthikeyan, B. (2006). Artificial neural network-embedded expert system for the design of canopy fabrics. Journal of industrial textiles, 36(2), 111-123.
  • [24] Nurwaha, D., & Wang, X. H. (2012). Using intelligent control systems to predict textile yarn quality. Fibres & Textiles in Eastern Europe, 20(1), 23-27.
  • [25] M.A . Tahir , A . Bouridane , F. Kurugollu , Simultaneous feature selection and feature weighting using hybrid tabu search/ k -nearest neighbor classifier, Pattern Recognit. Lett. 28 (4) (2007) 438–446.
  • [26] Eroglu, D. Y., & Kilic, K. (2017). A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management. Information Sciences, 405, 18-32.
  • [27] Alam, M. R., Togneri, R., Sohel, F., Bennamoun, M., & Naseem, I. (2013, February). Linear regression-based classifier for audio visual person identification. In Communications, Signal Processing, and their Applications (ICCSPA), 2013 1st International Conference on (pp. 1-5). IEEE.
  • [28] Bühlmann, P., & Yu, B. (2003). Boosting with the L 2 loss: regression and classification. Journal of the American Statistical Association, 98(462), 324-339.
  • [29] G.H. John , P. Langley , Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., 1995, pp. 338–345 .
  • [30] D.W. Aha , D. Kibler , M.K. Albert , Instance-based learning algorithms, Mach. Learn. 6 (1) (1991) 37–66 .
  • [31] M. Hall , E. Frank , G. Holmes , B. Pfahringer , P. Reutemann , I.H. Witten , The Weka data mining software: an update, ACM SIGKDD Explorations Newsletter 11 (1) (2009) 10–18 .

Giriş Kalite Kontrol Sürecinde Kabul-Ret Kararı Destek Sistemi

Yıl 2019, Cilt: 7 Sayı: 3, 576 - 590, 27.09.2019

Duygu Yılmaz Eroğlu

https://doi.org/10.29109/gujsc.549890

Öz

Karar destek algoritması tasarlanırken
en önemli aşama, kullanıcıların beklentilerinin belirlenmesidir. Sonrasında
veriler, veri madenciliği çalışma alanına aktarılır, hazırlanarak en önemli
girdi parametreleri belirlenir, sistemi en iyi temsil eden ve örüntüdeki gizli
bilgileri ortaya çıkaran model kurulur. Ardından, modelin performans göstergesi
saptanır ve doğrulanmış sonuçlar değerlendirilir ya da karara destek olmak
üzere kullanıma sunulur. Bu çalışmada da iplik kalite kabul sürecinde, dört
adet girdi faktörünün yanında, kabul kararının verilmesinde çalışanların bilgi
birikimlerinin de dikkate alındığı bir sistem için öngörü destek algoritmaları tasarlanmıştır.
İlk algoritma, daha önce sınıflandırma çalışması için tasarlanıp doğrulanan
melez genetik algoritma olup mevcut çalışmaya adapte edilmiştir. Diğer
algoritma ise sinirsel ağlar temelli melez radyal tabanlı fonksiyondur ve probleme uygun
hale getirilerek kodlanmıştır. Gerçek üretim verilerinin kabul-ret kararı için
sınıflandırılması sürecinde, geliştirilen iki algoritmanın yanında literatürde
iyi bilinen bazı yöntemler kullanılarak performans karşılaştırması yapılmıştır. Melez
genetik algoritmanın performansı doğrulandıktan sonra, elde edilen en iyi
kromozom, sınıflandırma tahmin modeli olarak kullanılmıştır. Önerilen yönteme
göre, seçilen öznitelik değerleri, belirlenen katsayılar ile çarpılmış ve bir
eşik değeri ile karşılaştırılarak makul bir doğruluk oranı ile kabul-ret kararı
verilebilmiştir.  Makalenin
literatüre katkısı ise iki şekilde değerlendirilebilir. İlki, önerilen melez
genetik algoritmanın sınıflandırma performansının melez sinirsel ağlar yöntemi
ile karşılaştırılması, ikincisi, önerilen melez genetik algoritma sonucunda
elde edilen en iyi kromozomun iplik kalite kabul süreci için destek sistem
olarak kullanabilmesidir.

Anahtar Kelimeler

Melez genetik algoritma Melez radyal tabanlı fonksiyon Sınıflandırma

Kaynakça

  • [1] HILLAKER, H.: John Boyd, USAF Retired, Father of the F16, Code one magazine, July 1997.
  • [2] Galinec, D., & Steingartner, W. (2013). A Look at Observe, Orient, Decide and Act Feedback Loop, Pattern-Based Strategy and Network Enabled Capability for Organizations Adapting to Change. Acta Electrotechnica et Informatica, 13(2), 39.
  • [3] W. Ni , A Review and Comparative Study on Univariate Feature Selection Techniques, University of Cincinnati, 2012 Ph.D. thesis .
  • [4] Y. Saeys , I. Inza , P. Larrañaga , A review of feature selection techniques in bioinformatics, Bioinformatics 23 (19) (2007) 2507–2517 .
  • [5] J.R. Vergara , P.A. Estévez , A review of feature selection methods based on mutual information, Neural Comput. Appl. 24 (1) (2014) 175–186 .
  • [6] M. Seera , C.P. Lim , A hybrid intelligent system for medical data classification, Expert Syst. Appl. 41 (5) (2014) 2239–2249 .
  • [7] K. Bache, M. Lichman, UCI Machine Learning Repository, 2013 http://archive.ics.uci.edu/ml .
  • [8] Y. Marinakis , M. Marinaki , A hybridized particle swarm optimization with expanding neighborhood topology for the feature selection problem, in: Hybrid Metaheuristics, Springer, 2013, pp. 37–51 .
  • [9] S. Chatterjee , A. Bhattacherjee , Genetic algorithms for feature selection of image analysis-based quality monitoring model: an application to an iron mine, Eng. Appl. Artif. Intell. 24 (5) (2011) 786–795.
  • [10] C.-F. Tsai , W. Eberle , C.-Y. Chu , Genetic algorithms in feature and instance selection, Knowl. Based. Syst. 39 (2013) 240–247 .
  • [11] N. Das , R. Sarkar , S. Basu , M. Kundu , M. Nasipuri , D.K. Basu , A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application, Appl. Soft. Comput. 12 (5) (2012) 1592–1606 .
  • [12] H. Du., Data Mining Techniques and Applications: an introduction, Cengage Learning EMEA (2010).
  • [13] Sousa, T., Silva, A., & Neves, A. (2004). Particle swarm based data mining algorithms for classification tasks. Parallel computing, 30(5-6), 767-783.
  • [14] M. Zhao , C. Fu , L. Ji , K. Tang , M. Zhou , Feature selection and parameter optimization for support vector machines: a new approach based on genetic algorithm with feature chromosomes, Expert Syst. Appl. 38 (5) (2011) 5197–5204.
  • [15] S. Li , H. Wu , D. Wan , J. Zhu , An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine, Knowl. Based Syst. 24 (1) (2011) 40–48.
  • [16] A . Özçift , A . Gülten , Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases, Digital Signal Process. 23 (1) (2013) 230–237.
  • [17] Haykin, S. S., Haykin, S. S., Haykin, S. S., & Haykin, S. S. (2009). Neural networks and learning machines (Vol. 3). Upper Saddle River: Pearson.
  • [18] Fu, X., & Wang, L. (2003). Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33(3), 399-409.
  • [19] Janik, P., & Lobos, T. (2006). Automated classification of power-quality disturbances using SVM and RBF networks. IEEE Transactions on Power Delivery, 21(3), 1663-1669.
  • [20] Yildirim, P., Birant, D., & Alpyildiz, T. (2018). Data mining and machine learning in textile industry. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1228.
  • [21] Akyol, U., Tüfekci, P., Kahveci, K., & Cihan, A. (2015). A model for predicting drying time period of wool yarn bobbins using computational intelligence techniques. Textile Research Journal, 85(13), 1367-1380.
  • [22] Yildiz, Z., Dal, V., Ünal, M., & Yildiz, K. (2013). Use of artificial neural networks for modelling of seam strength and elongation at break. Fibres & Textiles in Eastern Europe.
  • [23] Behera, B. K., & Karthikeyan, B. (2006). Artificial neural network-embedded expert system for the design of canopy fabrics. Journal of industrial textiles, 36(2), 111-123.
  • [24] Nurwaha, D., & Wang, X. H. (2012). Using intelligent control systems to predict textile yarn quality. Fibres & Textiles in Eastern Europe, 20(1), 23-27.
  • [25] M.A . Tahir , A . Bouridane , F. Kurugollu , Simultaneous feature selection and feature weighting using hybrid tabu search/ k -nearest neighbor classifier, Pattern Recognit. Lett. 28 (4) (2007) 438–446.
  • [26] Eroglu, D. Y., & Kilic, K. (2017). A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management. Information Sciences, 405, 18-32.
  • [27] Alam, M. R., Togneri, R., Sohel, F., Bennamoun, M., & Naseem, I. (2013, February). Linear regression-based classifier for audio visual person identification. In Communications, Signal Processing, and their Applications (ICCSPA), 2013 1st International Conference on (pp. 1-5). IEEE.
  • [28] Bühlmann, P., & Yu, B. (2003). Boosting with the L 2 loss: regression and classification. Journal of the American Statistical Association, 98(462), 324-339.
  • [29] G.H. John , P. Langley , Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., 1995, pp. 338–345 .
  • [30] D.W. Aha , D. Kibler , M.K. Albert , Instance-based learning algorithms, Mach. Learn. 6 (1) (1991) 37–66 .
  • [31] M. Hall , E. Frank , G. Holmes , B. Pfahringer , P. Reutemann , I.H. Witten , The Weka data mining software: an update, ACM SIGKDD Explorations Newsletter 11 (1) (2009) 10–18 .
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Tasarım ve Teknoloji
Yazarlar

Duygu Yılmaz Eroğlu 0000-0002-7730-2707

Yayımlanma Tarihi 27 Eylül 2019
Gönderilme Tarihi 5 Nisan 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 7 Sayı: 3

Kaynak Göster

APA Yılmaz Eroğlu, D. (2019). Giriş Kalite Kontrol Sürecinde Kabul-Ret Kararı Destek Sistemi. Gazi University Journal of Science Part C: Design and Technology, 7(3), 576-590. https://doi.org/10.29109/gujsc.549890

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