Abstract
In the steel industry, specifically alloy steel, creating different defected product can impose a high cost for steel product manufacturer. This paper is focused on an intelligent multiple classes fault diagnosis in steel plates to help operational decision makers to organise an effective and efficient manufacturing production. Treebagger random forest, machine learning ensemble method, and support vector machine are proposed as multiple classifiers. The experimental results are further on compared with results in previous researches. Experimental results encourage further research in application intelligent fault diagnosis in steel plates decision support system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aldrich, C., Auret, L.: Unsupervised process monitoring and fault diagnosis with machine learning methods. Springer (2013)
Deng, S., Lin, S.Y., Chang, W.L.: Application of multiclass support vector machines for fault diagnosis of field air defence gun. Expert Systems with Applications 38(5), 6007–6013 (2011)
Semeion, Research Center of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy, UCI Machine Learning Repository (2010), http://archive.ics.uci.edu/ml (accessed July 29, 2013)
Fakhr, M., Elsayad, A.M.: Steel Plates Faults Diagnosis with Data Mining Models. Journal of Computer Science 8(4), 506–514 (2012)
Abraham, A., Corchado, E., Corchado, J.M.: Hybrid Learning Machines. Neurocomputing 72(13-15), 2729–2730 (2009)
Corchado, E., Abraham, A., de Carvalho, A.: Hybrid intelligent algorithms and applications. Information Science 180, 2633–2634 (2010)
Simić, D., Kovačević, I., Simić, S.: Insolvency Prediction for Assessing Corporate Financial Health. Logic Journal of the IGPL 20(3), 536–549 (2012)
Wozniak, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Information Fusion 16, 3–17 (2014)
Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A., Snašel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)
Buscema, M., Terzi, S., Tastle, W.: A new meta-classifier. In: Proceedings of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–7 (2010)
Buscema, M., Tastle, W.J., Terzi, S.: Meta Net: A New Meta-Classifier Family. In: Tastle, W.J. (ed.) Data Mining Applications Using Artificial Adaptive Systems, pp. 141–182. Springer (2012)
Aldrich, C., Auret, L.: Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer (2013)
Zapico, A., Molisani, L.: Fault Diagnosis on Steel Structures Using Artificial Neural Networks. Mecanica Computacional 28(3), 181–188 (2009)
Amid, E., Aghdam, S.R., Amindavar, H.: Enhanced Performance for Support Vector Machines as Multi-class Classifiers in Steel Surface Defect Detection. World Academy of Science, Engineering and Technology 6(7), 1096–1100 (2012)
Vapnik, V.N.: Estimation of Dependences Based on Empirical Data. Springer Series in Statistics. Springer-Verlag New York, Inc., Secaucus (1982)
Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc., New York (1995)
Vapnik, V.N.: Statistical learning theory. Wiley (1998)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2(2), 121–167 (1998)
Wozniak, M.: Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer Series in Studies in Computational Intelligence (2013)
Corte, C., Vapnik, V.: Support–Vector Networks. Machine Learning 20(3), 273–297 (1995)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis & Recognition, Montreal, Canada, vol. 1, pp. 278–282 (1995)
Okun, O.: Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations. Medical Information Science Reference (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Simić, D., Svirčević, V., Simić, S. (2014). An Approach of Steel Plates Fault Diagnosis in Multiple Classes Decision Making. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-07617-1_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07616-4
Online ISBN: 978-3-319-07617-1
eBook Packages: Computer ScienceComputer Science (R0)