<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.</p></abstract>

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine learning and data mining. The fuzzy rule-based classification approach proposed in current paper exploits the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative nature of those methods - the rules are split in a way that makes efficient rule consolidation feasible and rule consolidation itself is capable of further error reduction - is demonstrated in a number of experiments with nine benchmark classification problems. Further complexity reduction, if necessary, is provided by rule compression.

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Laboratory for Proactive Technologies, Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn,

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation"}

This paper addresses the issue how to strike a good balance between accuracy and compactness in classification systems - still an important question in machine...

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Data publikacji: 23 lut 2017

Zakres stron: 137 - 147

DOI: https://doi.org/10.1515/jaiscr-2017-0010

Słowa kluczowe
pattern recognition, fuzzy classification, complexity reduction

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Design of Fuzzy Rule-based Classifiers through Granulation and Consolidation

Data publikacji: 23 lut 2017

Zakres stron: 137 - 147

DOI: https://doi.org/10.1515/jaiscr-2017-0010

Słowa kluczowepattern recognition, fuzzy classification, complexity reduction

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
pattern recognition, fuzzy classification, complexity reduction