1

C

Linguistic decision tree induction

J. M. Rossiter and T. P. Martin Ed., the Proceedings of the 2003 UK workshop on Computational Intelligence (UKCI-03), pp. 268-271, Bristol, UK. ISBN: 0862925371.

2

C

Zengchang Qin and Jonathan Lawry (2004)

A Tree-structured classification model based on label semantics

The Proceedings of the 10th International Conference of Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU-04), pp. 261-268, Perugia, Italy.

3

C

Zengchang Qin and Jonathan Lawry (2005)

Hybrid Bayesian estimation trees based on label semantics

L. Godo (Ed.), The Proceedings of Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-05), Lecture Notes in Artificial Intelligence (LNAI 3571), pp. 896-907, Springer.

4

C

Linguistic rule induction based on a random set semantics

Fuzzy Logic, Soft Computing and Computational Intelligence - the proceedings of IFSA world congress (IFSA-05), pp. 1398-1404, Springer-Tsinghua.

5

C

Zengchang Qin and Jonathan Lawry (2005)

Prediction trees using linguistic modelling

Fuzzy Logic, Soft Computing and Computational Intelligence - the proceedings of IFSA world congress, pp. 1643-1649, Springer-Tsinghua.

(Best Student Paper Award)

6

J

Zengchang Qin and Jonathan Lawry (2005)

Decision tree learning with fuzzy labels

Information Sciences, Vol. 172/1-2, pp. 91-129.

Zengchang Qin and Jonathan Lawry (2006)

High level fuzzy labels for vague concepts

Soft Methods for Integrated Uncertainty Modeling, Lawry, Miranda, Bugarin, Li, Angeles Gil, Grzegorzewski and Hryniewicz (Eds.), Advances in Soft Computing 6 , Vol.37, pp. 317-324, Springer.

Fuzziness and performance: an empirical study with linguistic decision trees

Foundations of Fuzzy Logic and Soft Computing, Lecture Notes in Artificial Intelligence (LNAI 4529), pp. 407-416, Springer.

Fuzzy label semantics for data mining

Forgingthe New Frontiers: Fuzzy Pioneers II, Ed. M. Nikravesh, J. Kacprzyk, and L. A. Zadeh, Studies in Fuzziness and Soft Computing, pp. 269-297, Springer. 

Knowledge discovery in a framework for modeling with words 

Soft Computing for Knowledge Discovery and Data Mining, Ed. O. Maimon and L. Rokach, pp. 241-276, Springer.

LFOIL: Linguistic rule induction in the label semantics framework

Fuzzy Sets and Systems, Vol. 159/4, pp. 435-448.

A dissimilarity measure of logical expressions

The Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC 2010),pp. 109‐203, IEEE Press.

Clustering data and imprecise concepts

Proceedings of IEEE-FUZZ, pp. 603-608.

Prediction and query evaluation using linguistic decision trees

Applied Soft Computing, Vol. 11, pp. 3916-3928.