Prediction and query evaluation using linguistic decision trees

Qin Zengchang, Jonathan Lawry

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

Abstract

Linguistic decision tree (LDT) is a tree-structured model based on a framework for “Modelling with Words”. In previous research [15] and [17], 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 [13] on a variety of data sets. Finally, a method for linguistic query evaluation is discussed and supported with an example.
Original languageEnglish
Pages (from-to)3916–3928
JournalApplied Soft Computing
Publication statusPublished - 2011

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