Abstract
When applying Machine Learning technology to real-world applications, such as visual quality inspection, several practical issues need to be taken care of. One problem is posed by the reality that usually there are multiple human operators doing the inspection, who will inevitable contradict each other occasionally. In this paper a framework is proposed which is able to deal with this issue, based on trained ensembles of classifiers. Most ensemble techniques have however difficulties learning in these circumstances. Therefore several novel enhancements to the Grading ensemble technique are proposed within this framework – called Active Grading. The Active Grading algorithm is evaluated on data obtained from a real-world industrial system for visual quality inspection of the printing of labels on CDs, which was labelled independently by four different human operators and their supervisor, and compared to the standard Grading algorithm and a range of other ensemble (classifier fusion) techniques.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Castillo, E., Alvarez, E.: Expert Systems: Uncertainty and Learning. Springer, New York (2007)
Malamas, E., Petrakis, E., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools. Image and Vision Computing 21 (2003)
Juran, J., Gryna, F.: Juran’s Quality Control Handbook, 4th edn. McGraw-Hill, New York (1988)
Seewald, A., Fürnkranz, J.: An evaluation of grading classifiers. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 115–124. Springer, Heidelberg (2001)
Sannen, D., Nuttin, M., Smith, J., Tahir, M.A., Caleb-Solly, P., Lughofer, E., Eitzinger, C.: An on-line interactive self-adaptive image classification framework. In: Gasteratos, A., Vincze, M., Tsotsos, J. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 171–180. Springer, Heidelberg (2008)
Govindaraju, M., Pennathur, A., Mital, A.: Quality improvement in manufacturing through human performance enhancement. Integrated Manufacturing Systems 12(5) (2001)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley & Sons, New York (2000)
Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Chichester (2004)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: A survey and categorisation. Information Fusion 6(1), 5–20 (2005)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)
Kuncheva, L., Whitaker, C., Shipp, C., Duin, R.: Limits on the majority vote accuracy in classifier fusion. Pattern Analysis & Applications 6(1), 22–31 (2003)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Cho, S., Kim, J.: Combining multiple neural networks by fuzzy integral for robust classification. IEEE Transactions on Systems, Man, and Cybernetics 25(2), 380–384 (1995)
Kuncheva, L., Bezdek, J., Duin, R.: Decision templates for multiple classifier fusion: An experimental comparison. Pattern Recognition 34(2), 299–314 (2001)
Rogova, G.: Combining the results of several neural network classifiers. Neural Networks 7(5), 777–781 (1994)
Sannen, D., Van Brussel, H., Nuttin, M.: Classifier fusion using Discounted Dempster-Shafer combination. In: Poster Proceedings of the 5th International Conference on Machine Learning and Data Mining, pp. 216–230 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sannen, D., Van Brussel, H. (2009). Active Grading Ensembles for Learning Visual Quality Control from Multiple Humans. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-02326-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02325-5
Online ISBN: 978-3-642-02326-2
eBook Packages: Computer ScienceComputer Science (R0)