Fuzzy-based feature and instance recovery

Liu, Shigang, Zhang, Jun, Wang, Yu and Xiang, Yang 2016, Fuzzy-based feature and instance recovery. In Nguyen, Ngoc Thanh, Trawinski, Bogdan, Fujita, Hamido and Hong, Tzung-Pei (ed), Intelligent information and database systems : 8th Asian conference, ACIIDS 2016, Da Nang, Vietnam, March 14-16, 2016, proceedings, part I, Springer, Berlin, Germany, pp.605-615, doi: 10.1007/978-3-662-49381-6_58.

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Title Fuzzy-based feature and instance recovery
Author(s) Liu, Shigang
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Wang, YuORCID iD for Wang, Yu orcid.org/0000-0002-9807-2293
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Title of book Intelligent information and database systems : 8th Asian conference, ACIIDS 2016, Da Nang, Vietnam, March 14-16, 2016, proceedings, part I
Editor(s) Nguyen, Ngoc Thanh
Trawinski, Bogdan
Fujita, Hamido
Hong, Tzung-Pei
Publication date 2016
Series Lecture notes in computer science
Chapter number 58
Total chapters 77
Start page 605
End page 615
Total pages 11
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) imbalanced data
information decomposition
Summary The severe class distribution shews the presence of underrepresented 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.
ISBN 9783662493816
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-662-49381-6_58
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083072

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