IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i4p1550147720916404.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720916404
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1550147720916404?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. DE CNUDDE, Sofie & MARTENS, David & EVGENIOU, Theodoros & PROVOST, Foster, 2017. "A benchmarking study of classification techniques for behavioral data," Working Papers 2017005, University of Antwerp, Faculty of Business and Economics.
    2. Liao, Jui-Jung & Shih, Ching-Hui & Chen, Tai-Feng & Hsu, Ming-Fu, 2014. "An ensemble-based model for two-class imbalanced financial problem," Economic Modelling, Elsevier, vol. 37(C), pages 175-183.
    3. Abha Sharma & Pushpendra Kumar & Kanojia Sindhuben Babulal & Ahmed J. Obaid & Harshita Patel, 2022. "Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(4), pages 1-15, August.
    4. Pancheng Wang & Shasha Li & Haifang Zhou & Jintao Tang & Ting Wang, 2019. "Cited text spans identification with an improved balanced ensemble model," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1111-1145, September.
    5. Keng-Hoong Ng & Chin-Kuan Ho & Somnuk Phon-Amnuaisuk, 2012. "A Hybrid Distance Measure for Clustering Expressed Sequence Tags Originating from the Same Gene Family," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-14, October.
    6. Yan Li & Manoj Thomas & Kweku-Muata Osei-Bryson & Jason Levy, 2016. "Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management," IJERPH, MDPI, vol. 13(12), pages 1-17, December.
    7. Neda Abdelhamid & Arun Padmavathy & David Peebles & Fadi Thabtah & Daymond Goulder-Horobin, 2020. "Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.
    8. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 2018. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 20(2), pages 401-416, April.
    9. Vilém Novák & Soheyla Mirshahi, 2021. "On the Similarity and Dependence of Time Series," Mathematics, MDPI, vol. 9(5), pages 1-14, March.
    10. Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Escanciano García-Miranda, Carmen & Sánchez Lasheras, Fernando, 2018. "Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models," Resources Policy, Elsevier, vol. 59(C), pages 95-102.
    11. Ionuţ ŢĂRANU, 2016. "Data mining in healthcare: decision making and precision," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(4), pages 33-40, May.
    12. Li, Hailin, 2017. "Distance measure with improved lower bound for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 622-637.
    13. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    14. Hady Suryono & Heri Kuswanto & Nur Iriawan, 2022. "Two-Phase Stratified Random Forest for Paddy Growth Phase Classification: A Case of Imbalanced Data," Sustainability, MDPI, vol. 14(22), pages 1-13, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:16:y:2020:i:4:p:1550147720916404. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.