@inproceedings{21101a867e9846ea8bd36cb4cb26dc74,
title = "Hybrid Bayesian estimation trees based on label semantics",
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.",
author = "Zengchang Qin and Jonathan Lawry",
year = "2005",
doi = "10.1007/11518655\_75",
language = "英语",
isbn = "3540273263",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "896--907",
booktitle = "Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 8th European Conference, ECSQARU 2005, Proceedings",
address = "德国",
note = "8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2005 ; Conference date: 06-07-2005 Through 08-07-2005",
}