Boosting the performance of the fuzzy min-max neural network in pattern classification tasks

Chen, Kok Yeng, Lim, Chee Peng and Harrison, Robert F. 2006, Boosting the performance of the fuzzy min-max neural network in pattern classification tasks, Advances in soft computing, vol. 34, pp. 373-387.

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Title Boosting the performance of the fuzzy min-max neural network in pattern classification tasks
Author(s) Chen, Kok Yeng
Lim, Chee Peng
Harrison, Robert F.
Journal name Advances in soft computing
Volume number 34
Start page 373
End page 387
Total pages 15
Publisher Springer
Place of publication Berlin, Germany
Publication date 2006
ISSN 1615-3871
Summary In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed.
Notes This paper was presented at the 9th Online World Conference on Soft Computing in Industrial Applications 2004.
Language eng
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30050269

Document type: Journal Article
Collection: Institute for Frontier Materials
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