Abstract
The chapter provides an overview of decision tree (DT) induction. Its main purpose is to introduce the reader to the major concepts underlying this data mining technique, particularly those that are relevant to the chapters that involve the use of this technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breiman L, Friedman J, Olshen R, Stone J (1984) Classification and regression trees. Wadsworth Inc., Belmont
Fournier D, Cremilleux B (2002) A quality index for decision tree pruning. Knowl-Based Syst 15:37–43
Kim H, Koehler G (1995) Theory and practice of decision tree induction. Omega 23(6):637–652
Ko M, Osei-Bryson K-M (2002) A regression tree based exploration of the impact of information technology investments on firm level productivity. European conference of information systems, pp 507–517
Osei-Bryson K-M (2004) Evaluation of decision trees: a multi-criteria approach. Comput Oper Res 31(11):1933–1945
Osei-Bryson K-M, Kendall Giles K (2004) An exploration of a set entropy-based hybrid splitting methods for decision tree induction. J Database Manage 15(3):1–17
Osei-Bryson K-M (2007) Post-pruning in decision tree induction using multiple performance measures. Comput Oper Res 34(11):3331–3345
Quinlan J (1993) C4.5 Programs for machine learning. Morgan Kaufmann, San Mateo
Taylor P, Silverman B (1993) Block diagrams and splitting criteria for classification trees. Stat Comput 3(4):147–161
Torgo L (1999) Predicting the density of algae communities using local regression trees. Proceedings of the European congress on intelligent techniques and soft computing (EUFIT’99)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Osei-Bryson, KM. (2014). Overview on Decision Tree Induction. In: Osei-Bryson, KM., Ngwenyama, O. (eds) Advances in Research Methods for Information Systems Research. Integrated Series in Information Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-9463-8_3
Download citation
DOI: https://doi.org/10.1007/978-1-4614-9463-8_3
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-9462-1
Online ISBN: 978-1-4614-9463-8
eBook Packages: Business and EconomicsBusiness and Management (R0)