Performing Variable Selection by Multiobjective Criterion: An Application to Mobile Payment

  • Alberto Guillén
  • Luis-Javier Herrera
  • Francisco Liébana
  • Oresti Baños
  • Ignacio Rojas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9095)


The rapid growth social networks have led many companies to use mobile payment systems as business sales tools. As these platforms have an increasing acceptance among the consumers, the main goal of this research is to analyze the individuals’ use intention of these systems in a social network environment. The problem of variable selection arises in this context as key to understand user’s behaviour. This paper compares several non-parametric criteria to perform variable selection and combines them in a multiobjective manner showing a good performance in the experiments carried out and validated by experts.


Mutual Information Variable Selection Pareto Front Mobile Payment Social Commerce 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Coello, C.A.C., Lamont, G.B., Van Veldhuisen, D.A.: Evolutionary algorithms for solving multi-objective problems. Springer (2007)Google Scholar
  2. 2.
    Davis, F.D.: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13(3), 319–340 (1989)CrossRefGoogle Scholar
  3. 3.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science 35, 982–1003 (1989)CrossRefGoogle Scholar
  4. 4.
    Eirola, E., Liitiinen, E., Lendasse, A., Corona, F., Verleysen, M.: Using the delta test for variable selection. In: ESANN, pp. 25–30 (2008)Google Scholar
  5. 5.
    Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)Google Scholar
  6. 6.
    Gefen, D.: E-commerce: The role of familiarity and trust. The International Journal of Management Science 28, 725–737 (2000)Google Scholar
  7. 7.
    Gerrard, P., Cunningham, J.B.: The diffusion of internet banking among Singapore consumers. International Journal of Bank Marketing 21(1), 16–28 (2003)CrossRefGoogle Scholar
  8. 8.
    Guillén, A., Del Moral, F.G., Herrera, L.J., Rubio, G., Rojas, I., Valenzuela, O., Pomares, H.: Using near-infrared spectroscopy in the classification of white and iberian pork with neural networks. Neural Computing and Applications 19(3), 465–470 (2010)CrossRefGoogle Scholar
  9. 9.
    Guillén, A., Pomares, H., González, J., Rojas, I., Valenzuela, O., Prieto, B.: Parallel multiobjective memetic rbfnns design and feature selection for function approximation problems. Neurocomputing 72(16), 3541–3555 (2009)CrossRefGoogle Scholar
  10. 10.
    Guillén, A., Sovilj, D., Lendasse, A., Mateo, F.: Minimising the delta test for variable selection in regression problems. International Journal of High Performance Systems Architecture 1(4), 269–281 (2008)CrossRefGoogle Scholar
  11. 11.
    Guillén, A., Sovilj, D., van Heeswijk, M., Herrera, L.J., Lendasse, A., Pomares, H., Rojas, I.: Evolutive approaches for variable selection using a non-parametric noise estimator. In: de Vega, F.F., Pérez, J.I.H., Lanchares, J. (eds.) Parallel Architectures and Bioinspired Algorithms. SCI, vol. 415, pp. 243–266. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Herrera, L.J., Fernandes, C.M., Mora, A.M., Migotina, D., Largo, R., Guillén, A., Rosa, A.C.: Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification. International journal of neural systems 23(3) (2013)Google Scholar
  13. 13.
    Liébana-Cabanillas, F.: El papel de los sistemas de pago en los nuevos entornos electrónicos, Doctoral Thesis, Marketing and Market Research Department, University of Granada (2012)Google Scholar
  14. 14.
    Long, X.X., Li, H.D., Fan, W., Xu, Q.S., Liang, Y.Z.: A model population analysis method for variable selection based on mutual information. Chemometrics and Intelligent Laboratory Systems 121, 75–81 (2012)CrossRefGoogle Scholar
  15. 15.
    Mallat, N., Rossi, M., Tuunainen, V., Rni, A.: The impact of use context on mobile services acceptance: the case of mobile ticketing. Information & Management 46(3), 190–195 (2009)CrossRefGoogle Scholar
  16. 16.
    May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.: Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling & Software 23(10), 1312–1326 (2008)CrossRefGoogle Scholar
  17. 17.
    National Statistics Institute, Survey about Equipment and Use of Information and Communication Technologies in Households (2012).
  18. 18.
    OCass, A., Fenech, T.: Web retailing adopction: exploring the nature of internet users web retailing behavior. Journal of Retailing and Consumer services 10, 81–94 (2003)CrossRefGoogle Scholar
  19. 19.
    Oliver, R.L.: A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research 17, 460–469 (1980)CrossRefGoogle Scholar
  20. 20.
    Pavlou, P.A.: A theory of Planned Behavior Perspective to the Consumer Adoption of Electronic Commerce. MIS Quarterly 30(1), 115–143 (2002)Google Scholar
  21. 21.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  22. 22.
    Premkumar, G., Rammurthy, K., Liu, H.: Internet Messaging: An Examination of the Impact of Attitudinal, Normative and Control Belief Systems. Information & Management 45, 451–457 (2008)CrossRefGoogle Scholar
  23. 23.
    Singh, J., Sirdeshmukh, D.: Agency and Trust Mechanisms in Consumer Satisfaction and Loyalty Judgments. Journal of the Academy of Marketing Science 28(1), 150–167 (2000)CrossRefGoogle Scholar
  24. 24.
    Smith, M., Brynjolfsson, E.: Consumer decision making at an internet shopbot: Brand still matters. The Journal of Industrial Economics 49(4), 541–558 (2001)CrossRefGoogle Scholar
  25. 25.
    Zhang, L.: Business model analysis for online social shopping companies. Case Company: Run To Shop Oy. Doctoral Thesis, Department of Business Technology/Logistics, Helsinki School of Economics (2009)Google Scholar
  26. 26.
    Herrera, L.J., Pomares, H., Rojas, I., Verleysen, M., Guilén, A.: Effective input variable selection for function approximation. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 41–50. Springer, Heidelberg (2006) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alberto Guillén
    • 1
  • Luis-Javier Herrera
    • 1
  • Francisco Liébana
    • 2
  • Oresti Baños
    • 3
  • Ignacio Rojas
    • 1
  1. 1.Department of Computer Architecture and TechnologyUniversity of GranadaGranadaSpain
  2. 2.Department of Marketing and Market ResearchUniversity of GranadaGranadaSpain
  3. 3.Ubiquitous Computing Lab (UCLab)Kyung Hee UniversitySeoulKorea

Personalised recommendations