Abstract
The framework of this paper is supervised learning using classification trees. Two types of variables play a role in the definition of the classification rule, namely a response variable and a set of predictors. The tree classifier is built up by a recursive partitioning of the prediction space such to provide internally homogeneous groups of objects with respect to the response classes. In the following, we consider the role played by an instrumental variable to stratify either the variables or the objects. This yields to introduce a tree-based methodology for conditional classification. Two special cases will be discussed to grow multiple discriminant trees and partial predictability trees. These approaches use discriminant analysis and predictability measures respectively. Empirical evidence of their usefulness will be shown in real case studies.
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
Aria, M., Siciliano, R.: Learning from Trees: Two-Stage Enhancements. In: CLADAG 2003, Book of Short Papers, Bologna, September 22-24, 2003, pp. 21–24. CLUEB, Bologna (2003)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, Belmont C.A. Wadsworth (1984)
Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications. Springer, Heidelberg (1979)
Gray, L.N., Williams, J.S.: Goodman and Kruskal’s tau b: multiple and partial analogs. In: Proceedings of the Americal Statistical Association, pp. 444–448 (1975)
Bertold, M., Hand, D. (eds.): Intelligent Data Analysis, 2nd edn. Springer, New York (2003)
Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)
Hastie, T.J., Tibshirani, R.J., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Mola, F., Siciliano, R.: A two-stage predictive splitting algorithm in binary segmentation. In: Dodge, Y., Whittaker, J. (eds.) Computational Statistics: COMPSTAT 1992, pp. 179–184. Physica Verlag, Heidelberg (D) (1992)
Mola, F., Siciliano, R.: A Fast Splitting Procedure for Classification Thees. Statistics and Computing 7, 208–216 (1997)
Mola, F., Siciliano, R.: Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining. In: Roli, F., Kittler, J. (eds.) Proceedings of International Conference on Multiple Classifier Systems, Chia, June 24-26, 2002. LNCS, pp. 118–126. Springer, Heidelberg (2002)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Irvine, CA, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Siciliano, R., Mola, F.: Multivariate Data Analysis through Classification and Regression Trees. In: Computational Statistics and Data Analysis, vol. 32, pp. 285–301. Elsevier Science, Amsterdam (2000)
Siciliano, R., Conversano, C.: Decision Tree Induction. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Data Mining, vol. 2, pp. 242–248. IDEA Group. Inc., Hershey, USA (2005)
Siciliano, R., Aria, M., Conversano, C.: Harvesting trees: methods, software and applications. In: Proceedings in Computational Statistics: 16th Symposium of IASC (COMPSTAT 2004). Eletronical Edition (CD), Prague, August 23–27, 2004, Physica-Verlag, Heidelberg (2004)
Tutore, V.A., Siciliano, R., Aria, M.: Three Way Segmentation. In: Tutore, V.A., Siciliano, R., Aria, M. (eds.) Proceedings of Knowledge Extraction and Modelling (KNEMO06), IASC INTERFACE IFCS Workshop, September 4-6, 2006, Capri (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tutore, V.A., Siciliano, R., Aria, M. (2007). Conditional Classification Trees Using Instrumental Variables. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-74825-0_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74824-3
Online ISBN: 978-3-540-74825-0
eBook Packages: Computer ScienceComputer Science (R0)