Abstract
Decisions made by workers in their daily routine have an environmental impact. The LOCAW project has analyzed the drivers and barriers for an employee to choose a particular option in large organizations. In this project, Agent-Based Models (ABM) seek to clarify interactions among relevant actors and provide insights into the necessary conditions to achieve more sustainable organizations. For theoretical and practical reasons, it was considered to use decision trees to represent the internal behavior of the agents in the model. This paper focuses on how to improve the generalization capabilities of these decision trees using feature selection and discretization techniques. The application of these techniques is intended to obtain simpler decision trees, but more accurate. Experimental results of three daily activities support the adequacy of the approach presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ehrentreich, N.: Agent-based modeling. Lecture Notes in Economics and Mathematical Systems 602 (2008)
Macal, C.M., North, M.J.: Tutorial on agent-based modelling and simulation. Journal of Simulation 4(3), 151–162 (2010)
Sánchez-Maroño, N., Alonso-Betanzos, A., Fontenla-Romero, O., Brinquis-Núñez, C., Polhill, J., Craig, T., Dumitru, A., García-Mira, R.: An agent-based model for simulating environmental behavior in an educational organization. Neural Processing Letters (2015)
Macy, M.W., Willer, R.: From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 143–166 (2002)
Quinlan, J.R.: C4. 5: programs for machine learning, vol. 1. Morgan Kaufmann (1993)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowledge and Information Systems 34(3), 483–519 (2013)
Hall, M.A.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1-3), 389–422 (2002)
Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: An enabling technique. Data Mining and Knowledge Discovery 6(4), 393–423 (2002)
Yang, Y., Webb, G.I.: Proportional k-interval discretization for naive-bayes classifiers. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 564–575. Springer, Heidelberg (2001)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Sánchez-Maroño, N., Alonso-Betanzos, A., Fontenla-Romero, O., Polhill, J.G., Craig, T. (2015). Designing Decision Trees for Representing Sustainable Behaviours in Agents. In: Bajo, J., et al. Trends in Practical Applications of Agents, Multi-Agent Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-319-19629-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-19629-9_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19628-2
Online ISBN: 978-3-319-19629-9
eBook Packages: EngineeringEngineering (R0)