On the Application of Preaggregation Functions to Fuzzy Pattern Tree

Pan Su, Tianhua Chen, Haoyu Mao, Jianyang Xie, Yitian Zhao, Jiang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Building transparent knowledge-based systems in the form of accurate and interpretable fuzzy rules is one of the significant applications of fuzzy set theory. The fuzzy connectives, i.e., T-norm/conorm, play the role of connecting fuzzy sets, which are essentially linguistic terms extracted from the knowledge embedded in a given data set. Fuzzy pattern tree is a recently proposed novel machine learning technique, which grows a hierarchical binary tree for each known class utilising conventional T-norms/conorms and aggregation operators. Preaggregation functions are recently proposed in the literature as a type of generalised aggregation functions, which have achieved successes in a number of applications. This paper proposes a preaggregation-based approach with application to the construction of fuzzy pattern tree. An experimental study is done to explore the performance of the fuzzy pattern tree where preaggregation functions are employed in comparison to that where conventional aggregation operators are utilised. Experimental results demonstrate that the performance of fuzzy pattern tree incorporated with the preaggregation function generated by Nilpotent minimum T-norm outperforms those with
alternative preaggregation functions and the commonly used ordered weighted averaging operators.
LanguageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems 2019
PublisherIEEE
Number of pages6
Publication statusAccepted/In press - 8 Apr 2019
Event2019 IEEE International Conference on Fuzzy Systems - New Orleans, United States
Duration: 23 Jun 201926 Jun 2019

Conference

Conference2019 IEEE International Conference on Fuzzy Systems
CountryUnited States
CityNew Orleans
Period23/06/1926/06/19

Fingerprint

Agglomeration
Mathematical operators
Binary trees
Fuzzy set theory
Knowledge based systems
Fuzzy rules
Fuzzy sets
Linguistics
Learning systems

Cite this

Su, P., Chen, T., Mao, H., Xie, J., Zhao, Y., & Liu, J. (Accepted/In press). On the Application of Preaggregation Functions to Fuzzy Pattern Tree. In IEEE International Conference on Fuzzy Systems 2019 IEEE.
Su, Pan ; Chen, Tianhua ; Mao, Haoyu ; Xie, Jianyang ; Zhao, Yitian ; Liu, Jiang . / On the Application of Preaggregation Functions to Fuzzy Pattern Tree. IEEE International Conference on Fuzzy Systems 2019. IEEE, 2019.
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title = "On the Application of Preaggregation Functions to Fuzzy Pattern Tree",
abstract = "Building transparent knowledge-based systems in the form of accurate and interpretable fuzzy rules is one of the significant applications of fuzzy set theory. The fuzzy connectives, i.e., T-norm/conorm, play the role of connecting fuzzy sets, which are essentially linguistic terms extracted from the knowledge embedded in a given data set. Fuzzy pattern tree is a recently proposed novel machine learning technique, which grows a hierarchical binary tree for each known class utilising conventional T-norms/conorms and aggregation operators. Preaggregation functions are recently proposed in the literature as a type of generalised aggregation functions, which have achieved successes in a number of applications. This paper proposes a preaggregation-based approach with application to the construction of fuzzy pattern tree. An experimental study is done to explore the performance of the fuzzy pattern tree where preaggregation functions are employed in comparison to that where conventional aggregation operators are utilised. Experimental results demonstrate that the performance of fuzzy pattern tree incorporated with the preaggregation function generated by Nilpotent minimum T-norm outperforms those withalternative preaggregation functions and the commonly used ordered weighted averaging operators.",
keywords = "Preaggregation, Aggregation, Fuzzy pattern tree, Knowledge base, Triangular norm",
author = "Pan Su and Tianhua Chen and Haoyu Mao and Jianyang Xie and Yitian Zhao and Jiang Liu",
year = "2019",
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language = "English",
booktitle = "IEEE International Conference on Fuzzy Systems 2019",
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Su, P, Chen, T, Mao, H, Xie, J, Zhao, Y & Liu, J 2019, On the Application of Preaggregation Functions to Fuzzy Pattern Tree. in IEEE International Conference on Fuzzy Systems 2019. IEEE, 2019 IEEE International Conference on Fuzzy Systems, New Orleans, United States, 23/06/19.

On the Application of Preaggregation Functions to Fuzzy Pattern Tree. / Su, Pan ; Chen, Tianhua; Mao, Haoyu; Xie, Jianyang; Zhao, Yitian; Liu, Jiang .

IEEE International Conference on Fuzzy Systems 2019. IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Zhao, Yitian

AU - Liu, Jiang

PY - 2019/4/8

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N2 - Building transparent knowledge-based systems in the form of accurate and interpretable fuzzy rules is one of the significant applications of fuzzy set theory. The fuzzy connectives, i.e., T-norm/conorm, play the role of connecting fuzzy sets, which are essentially linguistic terms extracted from the knowledge embedded in a given data set. Fuzzy pattern tree is a recently proposed novel machine learning technique, which grows a hierarchical binary tree for each known class utilising conventional T-norms/conorms and aggregation operators. Preaggregation functions are recently proposed in the literature as a type of generalised aggregation functions, which have achieved successes in a number of applications. This paper proposes a preaggregation-based approach with application to the construction of fuzzy pattern tree. An experimental study is done to explore the performance of the fuzzy pattern tree where preaggregation functions are employed in comparison to that where conventional aggregation operators are utilised. Experimental results demonstrate that the performance of fuzzy pattern tree incorporated with the preaggregation function generated by Nilpotent minimum T-norm outperforms those withalternative preaggregation functions and the commonly used ordered weighted averaging operators.

AB - Building transparent knowledge-based systems in the form of accurate and interpretable fuzzy rules is one of the significant applications of fuzzy set theory. The fuzzy connectives, i.e., T-norm/conorm, play the role of connecting fuzzy sets, which are essentially linguistic terms extracted from the knowledge embedded in a given data set. Fuzzy pattern tree is a recently proposed novel machine learning technique, which grows a hierarchical binary tree for each known class utilising conventional T-norms/conorms and aggregation operators. Preaggregation functions are recently proposed in the literature as a type of generalised aggregation functions, which have achieved successes in a number of applications. This paper proposes a preaggregation-based approach with application to the construction of fuzzy pattern tree. An experimental study is done to explore the performance of the fuzzy pattern tree where preaggregation functions are employed in comparison to that where conventional aggregation operators are utilised. Experimental results demonstrate that the performance of fuzzy pattern tree incorporated with the preaggregation function generated by Nilpotent minimum T-norm outperforms those withalternative preaggregation functions and the commonly used ordered weighted averaging operators.

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KW - Knowledge base

KW - Triangular norm

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Su P, Chen T, Mao H, Xie J, Zhao Y, Liu J. On the Application of Preaggregation Functions to Fuzzy Pattern Tree. In IEEE International Conference on Fuzzy Systems 2019. IEEE. 2019