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A review on classification of imbalanced data for wireless sensor networks

Author

Listed:
  • Harshita Patel
  • Dharmendra Singh Rajput
  • G Thippa Reddy
  • Celestine Iwendi
  • Ali Kashif Bashir
  • Ohyun Jo

Abstract

Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

Suggested Citation

  • Harshita Patel & Dharmendra Singh Rajput & G Thippa Reddy & Celestine Iwendi & Ali Kashif Bashir & Ohyun Jo, 2020. "A review on classification of imbalanced data for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:4:p:1550147720916404
    DOI: 10.1177/1550147720916404
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    References listed on IDEAS

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    1. Dharmendra Singh Rajput, 2019. "Review on recent developments in frequent itemset based document clustering, its research trends and applications," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 11(2), pages 176-195.
    2. Qiang Yang & Xindong Wu, 2006. "10 Challenging Problems In Data Mining Research," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 597-604.
    3. Xiang Yin & Kaiquan Zhang & Bin Li & Arun Kumar Sangaiah & Jin Wang, 2018. "A task allocation strategy for complex applications in heterogeneous cluster–based wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(8), pages 15501477187, August.
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    Cited by:

    1. Hong Zhang & Shigen Shen & Qiying Cao & Xiaojun Wu & Shaofeng Liu, 2020. "Modeling and analyzing malware diffusion in wireless sensor networks based on cellular automaton," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.
    2. Ashok Kumar P & Shiva Shankar G & Praveen Kumar Reddy Maddikunta & Thippa Reddy Gadekallu & Abdulrahman Al-Ahmari & Mustufa Haider Abidi, 2020. "Location Based Business Recommendation Using Spatial Demand," Sustainability, MDPI, vol. 12(10), pages 1-12, May.
    3. Minati, Ludovico & Li, Chao & Bartels, Jim & Chakraborty, Parthojit & Li, Zixuan & Yoshimura, Natsue & Frasca, Mattia & Ito, Hiroyuki, 2023. "Accelerometer time series augmentation through externally driving a non-linear dynamical system," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    4. Vijayalakshmi S & John A & Sunder R & Senthilkumar Mohan & Sweta Bhattacharya & Rajesh Kaluri & Guang Feng & Usman Tariq, 2020. "Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.
    5. Rupa Ch & Thippa Reddy Gadekallu & Mustufa Haider Abidi & Abdulrahman Al-Ahmari, 2020. "Computational System to Classify Cyber Crime Offenses using Machine Learning," Sustainability, MDPI, vol. 12(10), pages 1-16, May.

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