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.
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.
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
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