Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
SIRPSS - Sustainable Industrial Site Redevelopment Planning Support System Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A New Function for Ensemble Pruning

Authors : Souad Taleb Zouggar, Abdelkader Adla

Published in: Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We propose in this work a new function named Diversity and Accuracy for Pruning Ensembles (DAPE) which takes into account both accuracy and diversity to prune an ensemble of homogenous classifiers. A comparative study with a diversity based method and experimental results on several datasets show the effectiveness of the proposed method.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Unveiling Hidden Patterns in Flexible Medical Treatment Processes – A Process Mining Case Study
Literature
1.
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9, 1545–1588 (1997)CrossRef
2.
3.
Banfield, R.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: Ensemble diversity measures and their application to thinning. Inf. Fusion 6(1), 49–62 (2005)CrossRef
4.
Bhatnagar, V., Bhardwaj, M., Sharma, S., Haroon, S.: Accuracy-diversity based pruning of classifier ensembles. Prog. Artif. Intell. 2(2–3), 97–111 (2014)CrossRef
5.
Biau, G., Cérou, F., Guyader, A.: On the rate of convergence of the bagged nearest neighbor estimate. J. Mach. Learn. Res. 11, 687–712 (2010)
6.
Breiman, L.: Bagging predictors. Mach. Learn. 26(2), 123–140 (1996)
7.
Breiman, L.: Randomizing outputs to increase prediction accuracy. Mach. Learn. 40, 229–242 (2000)CrossRef
8.
9.
Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: Proceedings of the 21st International Conference on Machine Learning (2004)
10.
Cavalcanti, G.D.C., Oliveira, L.S., Moura, T.J.M., Carvalho, G.V.: Combining diversity measures for ensemble pruning. Pattern Recognit. Lett. 74, 38–45 (2016). ISSN 0167-8655CrossRef
11.
Qun, D., Ye, R., Liu, Z.: Considering diversity and accuracy simultaneously for ensemble pruning. Appl. Soft Comput. 58, 75–91 (2017). ISSN 1568-4946CrossRef
12.
Dietterich, T.G., Kong, E.B.: Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical report, Dept of Computer Science, Oregon State University, Covallis, Oregon (1995)
13.
Fan, W., Chu, F., Wang, H., Yu, P.S.: Pruning and dynamic scheduling of cost-sensitive ensembles. In: Eighteenth National Conference on Artificial Intelligence, pp. 146–151. American Association for Artificial Intelligence (2002)
14.
Fu, Q., Hu, S.X., Zhao, S.Y.: Clustering-based selective neural network ensemble. J. Zhejiang Univ. Sci. A 6(5), 387–392 (2005)CrossRef
15.
Guo, H., Liu, H., Li, R., Wu, C., Guo, Y., Xu, M.: Margin & diversity based ordering ensemble pruning. Neurocomputing 275, 237–246 (2017). ISSN 0925-2312CrossRef
16.
Hernández-Lobato, D., Martínez-Munoz, G.: A statistical instance-based pruning in ensembles of independent classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 364–369 (2009)CrossRef
17.
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRef
18.
Margineantu, D., Dietterich, T.G.: Pruning adaptive boosting. In: Proceedings of the 14th International Conference on Machine Learning, pp. 211–218. Morgan Kaufmann, San Francisco (1997)
19.
Markatopoulou, F., Tsoumakas, G., Vlahavas, I.: Instance-based ensemble pruning via multi-label classification. In: ICTAI 2010 (2010)
20.
Martínez-Muñoz, G., Suárez, A.: Aggregation ordering in bagging. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, pp. 258–263. Acta Press (2004)
21.
22.
23.
Partalas, I., Tsoumakas, G., Katakis, I., Vlahavas, I.: Ensemble pruning using reinforcement learning. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds.) SETN 2006. LNCS (LNAI), vol. 3955, pp. 301–310. Springer, Heidelberg (2006). https://​doi.​org/​10.​1007/​11752912_​31CrossRef
24.
Partalas, I., Tsoumakas, G., Vlahavas, I.: Focused ensemble selection: a diversity-based method for greedy ensemble selection. In: Ghallab, M., Spyropoulos, C.D., Fakotakis, N., Avouris, N.M. (eds.) ECAI 2008 - 18th European Conference on Artificial Intelligence. Proceedings of the Frontiers in Artificial Intelligence and Applications, Patras, Greece, 21–25 July 2008, vol. 178, pp. 117–121. IOS Press (2008)
25.
Partalas, I., Tsoumakas, G., Vlahavas, I.: An ensemble uncertainty aware measure for directed hill climbing ensemble pruning. Mach. Learn. 81, 257–282 (2010)CrossRef
26.
Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
27.
28.
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, Los Altos (2005)
29.
Zheng, Z., Webb, G.I.: Stochastic attribute selection committees. Technical report (TR C98/08), School of Computing and Mathematics, Deakin University, Australia (1998)
30.
Zhou, H., Zhao, X., Wang, X.: An effective ensemble pruning algorithm based on frequent patterns. Knowl.-Based Syst. 56, 79–85 (2014). ISSN 0950-7051CrossRef
Metadata
Title
A New Function for Ensemble Pruning
Authors
Souad Taleb Zouggar
Abdelkader Adla
Copyright Year
2018
Book
Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support

Print ISBN: 978-3-319-90314-9

Electronic ISBN: 978-3-319-90315-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-90315-6_15

Premium Partner

    Image Credits