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Ensemble selection in one-versus-one scheme – case study for cutting tools classification

Izabela Rojek Robert Burduk Paulina Heda
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | No. 1 | e136044 | DOI: 10.24425/bpasts.2021.136044
Keywords ensemble of classifiers ensemble selection one-vs-one decomposition cutting tool
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Abstract

The binary classifiers are appropriate for classification problems with two class labels. For multi-class problems, decomposition techniques, like one-vs-one strategy, are used because they allow the use of binary classifiers. The ensemble selection, on the other hand, is one of the most studied topics in multiple classifier systems because a selected subset of base classifiers may perform better than the whole set of base classifiers. Thus, we propose a novel concept of the dynamic ensemble selection based on values of the score function used in the one-vs-one decomposition scheme. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The proposed approach is compared with the static ensemble selection method based on the integration of base classifiers in geometric space, which also uses the one-vs-one decomposition scheme. In addition, other base classification algorithms are used to compare results in the conducted experiments. The obtained results demonstrate the effectiveness of our approach.

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Bibliography

  1.  C. Sammut and G. I. Webb, Encyclopedia of Machine Learning and Data Mining. Springer US, 2016.
  2.  M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning. MIT press, 2018.
  3.  S. Osowski and K. Siwek, “Local dynamic integration of ensemble in prediction of time series”, Bull. Pol. Ac.: Tech. 67(3), 517–525 (2019).
  4.  O. Sagi and L. Rokach, “Ensemble learning: A survey”, Wiley Interdiscip. Rev.-Data Mining Knowl. Discov. 8(4), e1249 (2018).
  5.  R.M. Cruz, R. Sabourin, and G.D. Cavalcanti, “Dynamic classifier selection: Recent advances and perspectives”, Inf. Fusion 41, 195–216 (2018).
  6.  O.A. Alzubi, J.A. Alzubi, M. Alweshah, I. Qiqieh, S. AlShami, and M. Ramachandran, “An optimal pruning algorithm of classifier ensembles: dynamic programming approach”, Neural Comput. Appl. 32, 16091–16107 (2020).
  7.  Y. Bian, Y. Wang, Y. Yao, and H. Chen, “Ensemble pruning based on objection maximization with a general distributed framework”, IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3766‒3774 (2020).
  8.  R.M. Cruz, D.V. Oliveira, G.D. Cavalcanti, and R. Sabourin, “Fire-des++: Enhanced online pruning of base classifiers for dynamic ensemble selection”, Pattern Recognit. 85, 149–160 (2019).
  9.  T.T. Nguyen, A.V. Luong, M.T. Dang, A.W.-C. Liew, and J. McCall, “Ensemble selection based on classifier prediction confidence”, Pattern Recognit. 100, 107104 (2020).
  10.  Z.-L. Zhang, X.-G. Luo, S. García, J.-F. Tang, and F. Herrera, “Exploring the effectiveness of dynamic ensemble selection in the one- versus-one scheme”, Knowledge-Based Syst. 125, 53–63 (2017).
  11.  M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, “Dynamic classifier selection for one-vs-one strategy: avoiding non-competent classifiers”, Pattern Recognit. 46(12), 3412–3424 (2013).
  12.  M. Pawlicki, A. Giełczyk, R. Kozik, and M. Choraś, “Faultprone software classes recognition via artificial neural network with granular dataset balancing”, in International Conference on Computer Recognition Systems 2019, Springer, 2019, pp. 130–140.
  13.  D. Rajeev, D. Dinakaran, and S. Singh, “Artificial neural network based tool wear estimation on dry hard turning processes of aisi4140 steel using coated carbide tool”, Bull. Pol. Ac.: Tech. 65(4), 553–559 (2017).
  14.  D. Więcek, A. Burduk, and I. Kuric, “The use of ann in improving efficiency and ensuring the stability of the copper ore mining process”, Acta Montanistica Slovaca 24(1), 1‒14 (2019).
  15.  P. Raja, R. Malayalamurthim, and M. Sakthivel, “Experimental investigation of cryogenically treated hss tool in turning on aisi1045 using fuzzy logic–taguchi approach”, Bull. Pol. Ac.: Tech. 67(4), 687–696 (2019).
  16.  T. Andrysiak and L. Saganowski, “Anomaly detection for smart lighting infrastructure with the use of time series analysis”, J. UCS 26(4), 508–527 (2020).
  17.  A. Burduk, K. Musiał, J. Kochańska, D. Górnicka, and A. Stetsenko, “Tabu search and genetic algorithm for production process scheduling problem”, LogForum 15, 181–189 (2019.
  18.  M. Choraś, M. Pawlicki, D. Puchalski, and R. Kozik, “Machine learning–the results are not the only thing that matters! what about security, explainability and fairness?”, in International Conference on Computational Science, Springer, 2020, pp. 615–628.
  19.  P. Zarychta, P. Badura, and E. Pietka, “Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis”, Bull. Pol. Ac.: Tech. 65(1), 63–70 (2017).
  20.  I. Rojek, E. Dostatni, and A. Hamrol, “Ecodesign of technological processes with the use of decision trees method”, in International Joint Conference SOCO’17-CISIS’17-ICEUTE’17, León, Spain, 2017, Springer, 2018, pp. 318–327.
  21.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign”, Bull. Pol. Ac.: Tech. 68(2), 199–206 (2020).
  22.  P. Prokopowicz, D. Mikołajewski, K. Tyburek, and E. Mikołajewska, “Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks”, Bull. Pol. Ac.: Tech. 68(2), 191–198 (2020).
  23.  S. Igari, F. Tanaka, and M. Onosato, “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”, Trans. Inst. Syst. Control Inform. Eng. 26(3), 87–94 (2013).
  24.  C. Tan and S. Ranjit, “An expert carbide cutting tools selection system for cnc lathe machine”, Int. Rev. Mech. Eng. 6(7), 1402–1405 (2012).
  25.  I. Rojek, “Technological process planning by the use of neural networks”, AI EDAM – AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf. 31(1), 1–15 (2017).
  26.  P. Heda, I. Rojek, and R. Burduk, “Dynamic ensemble selection – application to classification of cutting tools”, in International Conference on Computer Information Systems and Industrial Management LNCS(12133), Springer, 2020, pp. 345–354.
  27.  L.I. Kuncheva, Combining Pattern Classifiers. John Wiley & Sons, Inc., 2014.
  28.  E. Santucci, L. Didaci, G. Fumera, and F. Roli, “A parameter randomization approach for constructing classifier ensembles”, Pattern Recognit. 69, 1–13 (2017).
  29.  M. Mohandes, M. Deriche, and S. O. Aliyu, “Classifiers combination techniques: A comprehensive review”, IEEE Access 6, 19626–19639 (2018).
  30.  J. Yan, Z. Zhang, K. Lin, F. Yang, and X. Luo, “A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks”, Knowledge-Based Syst. 198. 105922 (2020).
  31.  P. Chaitra and R.S. Kumar, “A review of multi-class classification algorithms”, Int. J. Pure Appl. Math. 118(14), 17–26 (2018).
  32.  M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, “An overview of ensemble methods for binary classifiers in multi- class problems: Experimental study on onevs-one and one-vs-all schemes”, Pattern Recognit. 44(8), 1761–1776 (2011).
  33.  R. Burduk, “Integration base classifiers based on their decision boundary”, in International Conference on Artificial Intelligence and Soft Computing, Springer, 2017, pp. 13–20.
  34.  M.P. Groover, Fundamentals of modern manufacturing: materials, processes and systems, Willey, 2010.
  35.  M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks”, Inf. Process. Manage. 45, 427–437 (2009).
  36.  I. Rojek, “Classifier models in intelligent capp systems”, in Man-Machine Interactions, pp. 311–319, Springer, 2009.
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Authors and Affiliations

Izabela Rojek
1
ORCID: ORCID
Robert Burduk
2
ORCID: ORCID
Paulina Heda
2
  1. Institute of Computer Science, Kazimierz Wielki University, ul. Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  2. Faculty of Electronic, Wroclaw University of Science and Technology, ul. Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

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