Abstract
In order to improve pattern recognition performance of an individual classifier an ensemble of classifiers is used. One of the phases of creating the multiple classifier system is the selection of base classifiers which are used as the original set of classifiers. In this paper we propose the algorithm of the dynamic ensemble selection that uses median and quartile of correctly classified objects. The resulting values are used to define the decision schemes, which are used in the selection of the base classifiers process. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The obtained results clearly indicate that the proposed algorithm improves the classification measure. The improvement concerns the comparison with the ensemble of classifiers method without the selection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)
Ulaş, A., Semerci, M., Yıldız, O.T., Alpaydın, E.: Incremental construction of classifier and discriminant ensembles. Inf. Sci. 179(9), 1298–1318 (2009)
Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 8(4), e1249 (2018)
Kuncheva, L.I.: Combining Pattern Classifiers. Wiley, New York (2014). https://doi.org/10.1002/9781118914564
Britto Jr., A.S., Sabourin, R., Oliveira, L.E.: Dynamic selection of classifiers–a comprehensive review. Pattern Recogn. 47(11), 3665–3680 (2014)
Tan, C., Ranjit, S.: An expert carbide cutting tools selection system for CNC lathe machine. Int. Rev. Mech. Eng. 6(7), 1402–1405 (2012)
Igari, S., Tanaka, F., Onosato, M.: Customization of a micro process planning system for an actual machine tool based on updating a machining database and generating a database oriented planning algorithm. In: ASME/ISCIE 2012 International Symposium on Flexible Automation, pp. 35–42. American Society of Mechanical Engineers Digital Collection (2012)
Rojek, I.: Classifier models in intelligent CAPP systems. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AINSC 2009, vol. 59, pp. 311–319. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00563-3_32
Rojek, I.: Technological process planning by the use of neural networks. AI EDAM 31(1), 1–15 (2017)
Dey, A., Shaikh, S.H., Saeed, K., Chaki, N.: Modified majority voting algorithm towards creating reference image for binarization. In: Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.) Advanced Computing, Networking and Informatics - Volume 1. SIST, vol. 27, pp. 221–227. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07353-8_26
Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fus. 6(1), 63–81 (2005)
Przybyła-Kasperek, M., Wakulicz-Deja, A.: Comparison of fusion methods from the abstract level and the rank level in a dispersed decision-making system. Int. J. Gen Syst 46(4), 386–413 (2017)
Bloch, I.: Information combination operators for data fusion: a comparative review with classification. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 26(1), 52–67 (1996)
Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16(1), 66–75 (1994)
Burduk, R., Baczyńska, P.: Ensemble of classifiers with modification of confidence values. In: Saeed, K., Homenda, W. (eds.) CISIM 2016. LNCS, vol. 9842, pp. 473–480. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45378-1_42
Baczyńska, P., Burduk, R.: Classifier selection uses decision profiles in binary classification task. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 7. AISC, vol. 389, pp. 3–10. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-23814-2_1
Black, J.T., Kohser, R.A.: DeGarmo’s Materials and Processes in Manufacturing. Wiley, Chichester (2017)
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Mana. 45(4), 427–437 (2009). https://doi.org/10.1016/j.ipm.2009.03.002
Acknowledgment
This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology and Institute of Computer Science, Kazimierz Wielki University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Heda, P., Rojek, I., Burduk, R. (2020). Dynamic Ensemble Selection – Application to Classification of Cutting Tools. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-47679-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-47678-6
Online ISBN: 978-3-030-47679-3
eBook Packages: Computer ScienceComputer Science (R0)