A Broad Review on Different Imbalanced Dataset Classification Approaches

Authors

  • K. Suresh Babu Associate Professor, Department of Computer Science and Engineering, Narasaraopet Engineering College, Narasaraopet, AP. India
  • B. Vara Prasada Rao Professor, Department of Computer Science and Engineering, RVR&JC College of Engineering, Guntur, Andhra Pradesh, India.
  • Y. Narasimha Rao Professor, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
  • J. Hari Kiran Associate Professor, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
  • Sai Chandana Bolem Assistant Professor, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
  • Srinivasa Rao Battula Assistant Professor, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
  • Koteswara Rao Chittepureddi Assistant Professor, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
  • Y. Anil Kumar Research Scholar, VIT-AP University, Amaravati, Vijayawada, India
  • Ratna Babu Chekka Associate Professor, Department of Computer Science and Engineering, RVR&JC College of Engineering,Guntur, Andhra Pradesh, India.

Keywords:

Imbalanced data, data mining, deep learning, classifiers, over and under sampling, optimization algorithms

Abstract

The series problem in extensive data application is managing the imbalanced data. Hence, the imbalanced data classification system was introduced to collect the imbalanced data at the maximum possible rate. Several neural mechanisms have been designed to classify imbalanced data with a high accuracy rate. However, the complexity of the data makes the classification process difficult by increasing the computation cost, resource usage, and algorithm complexity. Hence, this review has detailed several classification model performances in different imbalanced databases. Finally, the performance analysis has been done to analyse the classification performance of each model. Hence, the robustness has been estimated based on precision, specificity, accuracy, and sensitivity. In addition, the merits and limitations of each model are also discussed in detail. Subsequently, based on the demerits, the classification models provided future directions to improve the imbalance data.

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Published

21.09.2023

How to Cite

Babu, K. S. ., Rao, B. V. P. ., Rao, Y. N. ., Kiran, J. H. ., Bolem, S. C. ., Battula, S. R. ., Chittepureddi, K. R. ., Kumar, Y. A. ., & Chekka, R. B. . (2023). A Broad Review on Different Imbalanced Dataset Classification Approaches. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 27–40. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3451

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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