Evaluating the Impact of Categorical Data Encoding and Scaling on Neural Network Classification Performance: The Case of Repeat Consumption of Identical Cultural Goods

  • Elena Fitkov-Norris
  • Samireh Vahid
  • Chris Hand
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


This article investigated the impact of categorical input encoding and scaling approaches on neural network sensitivity and overall classification performance in the context of predicting the repeat viewing propensity of movie goers. The results show that neural network out of sample minimum sensitivity and overall classification performance are indifferent to the scaling of the categorical inputs. However, the encoding of inputs had a significant impact on classification accuracy and utilising ordinal or thermometer encoding approaches for categorical inputs significantly increases the out of sample accuracy of the neural network classifier. These findings confirm that the impact of categorical encoding is problem specific for an ordinal approach, and support thermometer encoding as most suitable for categorical inputs. The classification performance of neural networks was compared against a logistic regression model and the results show that in this instance, the non-parametric approach does not offer any advantage over standard statistical models.


neural networks logistic regression categorical input encoding scaling 


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  1. 1.
    Paliwal, M., Kumar, U.A.: Neural networks and statistical techniques: A review of applications. ESWA 36(1), 2–17 (2009)Google Scholar
  2. 2.
    Brouwer, R.: A feed-forward network for input that is both categorical and quantitative. NN (2002)Google Scholar
  3. 3.
    Crone, S., Lessmann, S., Stahlbock, R.: The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing. EJOR 9(16), 781–800 (2006)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Niu, D., Wang, Y., Wu, D.D.: Power load forecasting using support vector machine and ant colony optimization. ESWA 37(3), 2531–2539 (2010)Google Scholar
  5. 5.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. JAIR 16, 321–357 (2002)zbMATHGoogle Scholar
  6. 6.
    Kim, K., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. ESWA 19(2), 125–132 (2000)MathSciNetGoogle Scholar
  7. 7.
    Collins, A., Hand, C., Linnell, M.: Analyzing repeat consumption of identical cultural goods: some exploratory evidence from moviegoing. J. Cult. Econ. 32(3), 187–199 (2008)CrossRefGoogle Scholar
  8. 8.
    Sharda, R., Delen, D.: Predicting box-office success of motion pictures with neural networks. ESWA 30(2), 243–254 (2006)Google Scholar
  9. 9.
    Zhang, L., Luo, J., Yang, S.: Forecasting box office revenue of movies with BP neural network. ESWA 36(2), 6580–6587 (2009)Google Scholar
  10. 10.
    Kim, S.: Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. Serv. Ind. J. 31(3), 441–468 (2011)CrossRefGoogle Scholar
  11. 11.
    Mazzatorta, P., Benfenati, E., Neagu, D., Gini, G.: The importance of scaling in data mining for toxicity prediction. JCICS 42(5), 1250–1255 (2002)Google Scholar
  12. 12.
    Viaene, S., Dedene, G., Derrig, R.: Auto claim fraud detection using Bayesian learning neural networks. ESWA 29(3), 653–666 (2005)Google Scholar
  13. 13.
    Sahoo, G., Ray, C., Mehnert, E., Keefer, D.: Application of artificial neural networks to assess pesticide contamination in shallow groundwater. SCTEN 367(1), 234–251 (2006)Google Scholar
  14. 14.
    Setiono, R., Thong, J., Yap, C.: Symbolic rule extraction from neural networks: An application to identifying organizations adopting IT. Inform. & Manage. 34(2), 91–101 (1998)CrossRefGoogle Scholar
  15. 15.
    Hsu, C.: Generalizing self-organizing map for categorical data. NN (2006)Google Scholar
  16. 16.
    Sakai, S., Kobayashi, K., Toyabe, S.I., Mandai, N., Kanda, T., Akazawa, T.: Comparison of the Levels of Accuracy of an Artificial Neural Network Model and a Logistic Regression Model for the Diagnosis of Acute Appendicitis. J. Med. Syst. 31(5), 357–364 (2007)CrossRefGoogle Scholar
  17. 17.
    Lai, K.K., Yu, L., Wang, S., Zhou, L.: Neural Network Metalearning for Credit Scoring. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006, Part I. LNCS, vol. 4113, pp. 403–408. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Basheer, I., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000)CrossRefGoogle Scholar
  19. 19.
    Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. NC 10(3), 215–236 (1996)Google Scholar
  20. 20.
    Carter, R.J., Dubchak, I., Holbrook, S.R.: A computational approach to identify genes for functional RNAs in genomic sequences. NAR 29(19), 3928–3938 (2001)Google Scholar
  21. 21.
    Haykin, S.: Neural Netwoks and Learning Machines, 3rd edn. Pearson Intenational Edition (2009) Google Scholar
  22. 22.
    Fernández-Navarro, F., Hervás-Martínez, C., García-Alonso, C., Torres-Jimenez, M.: Determination of relative agrarian technical efficiency by a dynamic over-sampling procedure guided by minimum sensitivity. ESWA 38(10), 12483–12490 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Elena Fitkov-Norris
    • 1
  • Samireh Vahid
    • 1
  • Chris Hand
    • 1
  1. 1.Kingston UniversityKingston-upon-ThamesUK

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