Linguistic decision tree (LDT) is a tree-structured model based on a framework for “Modelling with Words”. In previous research  and , an algorithm for learning LDTs was proposed and its performance on some benchmark classification problems were investigated and compared with a number of well known classifiers. In this paper, a methodology for extending LDTs to prediction problems is proposed and the performance of LDTs are compared with other state-of-art prediction algorithms such as a Support Vector Regression (SVR) system and Fuzzy Semi-Naive Bayes  on a variety of data sets. Finally, a method for linguistic query evaluation is discussed and supported with an example.
|Journal||Applied Soft Computing|
|Publication status||Published - 2011|