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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2016: Intelligent Information and Database Systems pp 605–615Cite as

Fuzzy-Based Feature and Instance Recovery

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9621))

Abstract

The severe class distribution shews the presence of under-represented data, which has great effects on the performance of learning algorithm, is still a challenge of data mining and machine learning. Lots of researches currently focus on experimental comparison of the existing re-sampling approaches. We believe it requires new ways of constructing better algorithms to further balance and analyse the data set. This paper presents a Fuzzy-based Information Decomposition oversampling (FIDoS) algorithm used for handling the imbalanced data. Generally speaking, this is a new way of addressing imbalanced learning problems from missing data perspective. First, we assume that there are missing instances in the minority class that result in the imbalanced dataset. Then the proposed algorithm which takes advantages of fuzzy membership function is used to transfer information to the missing minority class instances. Finally, the experimental results demonstrate that the proposed algorithm is more practical and applicable compared to sampling techniques.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne, VIC, 3125, Australia

    Shigang Liu, Jun Zhang, Yu Wang & Yang Xiang

Authors
  1. Shigang Liu
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  2. Jun Zhang
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  3. Yu Wang
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  4. Yang Xiang
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Corresponding author

Correspondence to Shigang Liu .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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© 2016 Springer-Verlag Berlin Heidelberg

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Liu, S., Zhang, J., Wang, Y., Xiang, Y. (2016). Fuzzy-Based Feature and Instance Recovery. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_58

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  • DOI: https://doi.org/10.1007/978-3-662-49381-6_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49380-9

  • Online ISBN: 978-3-662-49381-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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