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Semi-supervised Clustering in Fuzzy Min-Max Neural Network

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Advances in Information and Communication Technology (ICTA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 538))

Abstract

The Fuzzy Min max Neural Network (FMNN) developed by Simpson is defined as a neural network that forms hyperboxes for classification and prediction. This paper proposes an improvement in learning algorithm in FMNN using semi-supervised clustering method, called SS-FMM. The proposed model combines the advantages of supervised learning and those of unsupervised learning. Labeled a part of data is the additional information that is used in this semi-supervised clustering method. For evaluation purpose, this algorithm is implemented on two datasets including Shape sets from CS and Thyorid disease from UCI. A part from that, in this paper, some related algorithms in FMNN are also setup on these datasets in order to compare the accuracy with proposed algorithm. The test results show that the novel algorithm has the better performance.

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Author information

Authors and Affiliations

  1. Thai Nguyen Industry College, Thai Nguyen, Viet Nam

    Dinh Minh Vu & Viet Hai Nguyen

  2. Hung Yen University of Technology and Education, Hung Yen, Viet Nam

    Ba Dung Le

Authors
  1. Dinh Minh Vu
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  2. Viet Hai Nguyen
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  3. Ba Dung Le
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Corresponding authors

Correspondence to Dinh Minh Vu , Viet Hai Nguyen or Ba Dung Le .

Editor information

Editors and Affiliations

  1. School of Information Science、Area of Human Life Design , Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Masato Akagi

  2. Department of Computer Science, VNU University of Engineering and Technology, Hanoi, Vietnam

    Thanh-Thuy Nguyen

  3. Faculty of Information Technology, Thai Nguyen University of Information and Communication Technology, Thai Nguyen, Vietnam

    Duc-Thai Vu

  4. Thai Nguyen University of Information and Communication Technology, Thai Nguyen, Vietnam

    Trung-Nghia Phung

  5. School of Knowledge Science、Area of Knowledge Management , Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Van-Nam Huynh

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Vu, D.M., Nguyen, V.H., Le, B.D. (2017). Semi-supervised Clustering in Fuzzy Min-Max Neural Network. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_58

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  • DOI: https://doi.org/10.1007/978-3-319-49073-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49072-4

  • Online ISBN: 978-3-319-49073-1

  • eBook Packages: EngineeringEngineering (R0)

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