C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Machine Learning
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Modelling With Words: Learning, Fusion, and Reasoning Within a Formal Linguistic Representation Framework (Lecture Notes in Computer Science, 2873.)
A framework for linguistic modelling
Artificial Intelligence
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
LFOIL: Linguistic rule induction in the label semantics framework
Fuzzy Sets and Systems
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Fuzziness and Performance: An Empirical Study with Linguistic Decision Trees
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Decision tree learning with fuzzy labels
Information Sciences: an International Journal
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparative study on heuristic algorithms for generating fuzzydecision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Hybrid Bayesian estimation tree learning with discrete and fuzzy labels
Frontiers of Computer Science: Selected Publications from Chinese Universities
| Hi-index | 0.00 |
Abstract: Linguistic decision tree (LDT) is a tree-structured model based on a framework for ''Modelling with Words''. In previous research [15,17], 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.