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Missing information in imbalanced data stream: fuzzy adaptive imputation approach

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Abstract

From a real-world perspective, missing information is an ordinary scenario in data stream. Generally, missing data generate diverse problems in recognizing the pattern of data (i.e., clustering and classification). Particularly, missing data in data stream is a challenging topic. With imbalanced data, the problem of missing data greatly affects pattern recognition. As a solution to all these issues, this study puts forward an adaptive technique with fuzzy-based information decomposition method, which simultaneously solves the problem of incomplete data and overcomes the imbalanced data stream in a dataset. The main purpose of the proposed fuzzy adaptive imputation approach (FAIA) is to represent the effect of missing values in imbalance data stream and handle the missing data problem in imbalance data stream. FAIA is a single pass method. It considers adaptive selection of intervals based on all observed instances by using the interrelationship of attributes to identify correct interval for computing missing instances. Here, the interrelationship of two attributes means one attribute’s value of an instance depends on another attribute’s value of the same instance. In FAIA, after measuring all interval distances from a certain missing value, the least distance is selected for this missing value. Synthetic data of minority class are generated using the same process of missing value imputation for balancing data that is called oversampling. Instances of the datasets are divided into the chunks in data stream to balance data without any ensemble of previous chunks because missing values may misguide the future chunks. To demonstrate the performance of FAIA, the experiment is divided into three parts: missing data imputation, imbalanced information for offline data for data stream, and imbalanced information with missing value for offline data. Eleven numerical datasets with different dimensions from various repositories are considered for the computing performance of missing data imputation and imbalanced data without data stream. Four different datasets are also used to measure the performance of imbalanced data stream. In maximum measuring cases, the proposed method outperforms.

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Acknowledgements

This research work was supported by Special Grant of ICT Division (Ministry of Posts, Telecommunications and Information Technology), Bangladesh, Grant No. 56.00.0000.028.20.004.20-333. The authors would like to acknowledge Ministry of Higher Education, Malaysia for their partial support through the Fundamental Research Grant Scheme (FRGS), under Grant 203/PELECT/6071398.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Barishal, Barishal, 8200, Bangladesh

    Bohnishikha Halder, Md Manjur Ahmed & Rahat Hossain Faisal

  2. Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa

  3. School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia

    Nor Ashidi Mat Isa

  4. Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh

    Md. Mostafijur Rahman

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  1. Bohnishikha Halder
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  2. Md Manjur Ahmed
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  5. Rahat Hossain Faisal
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Correspondence to Md Manjur Ahmed or Nor Ashidi Mat Isa.

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Halder, ., Ahmed, M.M., Amagasa, T. et al. Missing information in imbalanced data stream: fuzzy adaptive imputation approach. Appl Intell 52, 5561–5583 (2022). https://doi.org/10.1007/s10489-021-02741-4

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