Hybrid bayesian estimation trees based on label semantics

Authors:
Zengchang Qin;Jonathan Lawry
Affiliations:
A.I. Group, Department of Engineering Mathematics, University of Bristol, Bristol, U.K;A.I. Group, Department of Engineering Mathematics, University of Bristol, Bristol, U.K
Venue:
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2005

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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.