nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

A Swarm Intelligence Approach in Undersampling Majority Class

verfasst von : Haya Abdullah Alhakbani, Mohammad Majid al-Rifaie

Erschienen in: Swarm Intelligence

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Over the years, machine learning has been facing the issue of imbalance dataset. It occurs when the number of instances in one class significantly outnumbers the instances in the other class. This study investigates a new approach for balancing the dataset using a swarm intelligence technique, Stochastic Diffusion Search (SDS), to undersample the majority class on a direct marketing dataset. The outcome of the novel application of this swarm intelligence algorithm demonstrates promising results which encourage the possibility of undersampling a majority class by removing redundant data whist protecting the useful data in the dataset. This paper details the behaviour of the proposed algorithm in dealing with this problem and investigates the results which are contrasted against other techniques.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Using Ant Colony Optimization to Build Cluster-Based Classification Systems

Nächstes Kapitel Optimizing PolyACO Training with GPU-Based Parallelization

Link to the dataset: http://mlr.cs.umass.edu/ml/datasets/Bank+Marketing.

al-Rifaie, M.M., Aber, A., Sayers, R., Choke, E., Bown, M.: Deploying swarm intelligence in medical imaging. In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 14–21. IEEE (2014)

Bahnsen, A.C., Aouada, D., Ottersten, B.: Ensemble of example-dependent cost-sensitive decision trees. arXiv preprint (2015). arXiv:1505.04637

Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM Sigkdd Explor. Newslett. 6(1), 20–29 (2004)CrossRef

Beckmann, M., Ebecken, N.F., de Lima, B.S.P.: A KNN undersampling approach for data balancing. J. Intell. Learn. Syst. Appl. 7(04), 104 (2015)

Bishop, J.: Stochastic searching networks. In: Procedings of the 1st IEE Conference on Artifical Neural Networks, pp. 329–331 (1989)

Bishop, J., Torr, P.: The stochastic search network. In: Linggard, R., Myers, D.J., Nightingale, C. (eds.) Neural Networks for Vision, Speech and Natural Language. BT Telecommunications Series, vol. 1, pp. 370–387. Springer, Cambridge (1992)CrossRef

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)MATH

Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM Sigkdd Explor. Newslett. 6(1), 1–6 (2004)CrossRef

Drown, D.J., Khoshgoftaar, T.M., Narayanan, R.: Using evolutionary sampling to mine imbalanced data. In: Sixth International Conference on Machine Learning and Applications, ICMLA 2007, pp. 363–368. IEEE (2007)

10.

Elsalamony, H.A.: Bank direct marketing analysis of data mining techniques. Int. J. Comput. Appl. 85(7), 12–22 (2014)

11.

Feng, G., Zhang, J.D., Liao, S.S.: A novel method for combining bayesian networks, theoretical analysis, and its applications. Pattern Recogn. 47(5), 2057–2069 (2014)CrossRefMATH

12.

García, V., Sánchez, J.S., Mollineda, R.A.: On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl. Based Syst. 25(1), 13–21 (2012)CrossRef

13.

Grech-Cini, H., McKee, G.T.: Locating the mouth region in images of human faces. In: Optical Tools for Manufacturing and Advanced Automation, pp. 458–465. International Society for Optics and Photonics (1993)

14.

Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005)CrossRef

15.

Hurley, S., Whitaker, R.M.: An agent based approach to site selection for wireless networks. In: Proceedings of the 2002 ACM Symposium on Applied Computing, pp. 574–577. ACM (2002)

16.

Japkowicz, N., et al.: Learning from imbalanced data sets: a comparison of various strategies. In: AAAI Workshop on Learning from Imbalanced Data Sets, vol. 68, pp. 10–15, Menlo Park, CA (2000)

17.

Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3(3), 263–286 (2001)CrossRefMATH

18.

Kubat, M., Matwin, S., et al.: Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, vol. 97, pp. 179–186, Nashville, USA (1997)

19.

McCluskey, A., Lalkhen, A.G.: Statistics II: central tendency and spread of data. Conti. Educ. Anaesth. Crit. Care Pain 7(4), 127–130 (2007)CrossRef

20.

Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22–31 (2014)CrossRef

21.

Moro, S., Laureano, R., Cortez, P.: Using data mining for bank direct marketing: an application of the crisp-dm methodology. In: Proceedings of the European Simulation and Modelling Conference, Eurosis (2011)

22.

Nasuto, S.: Resource allocation analysis of the stochastic diffusion search. Ph.D. thesis, University of Reading (1999)

23.

al Rifaie, M.M., Bishop, J.M.: Stochastic diffusion search review. J. Behav. Robot. 3, 155–173 (2013)

24.

Sun, Y., Wong, A.K., Kamel, M.S.: Classification of imbalanced data: a review. Int. J. Pattern Recogn. Artif. Intell. 23(04), 687–719 (2009)CrossRef

Titel: A Swarm Intelligence Approach in Undersampling Majority Class
verfasst von: Haya Abdullah Alhakbani
Mohammad Majid al-Rifaie
Verlag: Springer International Publishing
Buch: Swarm Intelligence
Print ISBN: 978-3-319-44426-0

Electronic ISBN: 978-3-319-44427-7

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-44427-7_19

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"