Abstract
Linguistic decision tree (LDT) [7] is a classification model based on a random set based semantics which is referred to as label semantics [4]. Each branch of a trained LDT is associated with a probability distribution over classes. In this paper, two hybrid learning models by combining linguistic decision tree and fuzzy Naive Bayes classifier are proposed. In the first model, an unlabelled instance is classified according to the Bayesian estimation given a single LDT. In the second model, a set of disjoint LDTs are used as Bayesian estimators. Experimental studies show that the first new hybrid models has both better accuracy and transparency comparing to fuzzy Naive Bayes and LDTs at shallow tree depths. The second model has the equivalent performance to the LDT model.
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
Preview
Unable to display preview. Download preview PDF.
References
Baldwin, J.F., Martin, T.P., Pilsworth, B.W.: Fril-Fuzzy and Evidential Reasoning in Artificial Intelligence. John Wiley & Sons Inc, Chichester (1995)
Blake, C., Merz, C.J.: UCI machine learning repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
Jeffrey, R.C.: The Logic of Decision. Gordon & Breach Inc., New York (1965)
Lawry, J.: A framework for linguistic modelling. Artificial Intelligence 155, 1–39 (2004)
Ling, C.X.: Decision tree with better ranking. In: Proceedings of International Conference on Machine Learning (ICML2003), Washington DC (2003)
Provost, F., Domingos, P.: Tree induction for probability-based ranking. Machine Learning 52, 199–215 (2003)
Qin, Z., Lawry, J.: Decision Tree Learning with Fuzzy Labels. To appear in Information Sciences (2005)
Qin, Z., Lawry, J.: ROC analysis of a linguistic decision tree merging algorithm. In: The Pro. of UK Workshop on Computational Intelligence, Loughborough, UK (2004)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Randon, N.J., Lawry, J.: Classification and query evaluation using modelling with words. Information Sciences, Special Issue - Computing with Words: Models and Applications (to appear)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999), http://www.cs.waikato.ac.nz/~ml/weka/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qin, Z., Lawry, J. (2005). Hybrid Bayesian Estimation Trees Based on Label Semantics. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_75
Download citation
DOI: https://doi.org/10.1007/11518655_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27326-4
Online ISBN: 978-3-540-31888-0
eBook Packages: Computer ScienceComputer Science (R0)