Imbalanced data classification problems endeavor to find a dependent variable in a skewed data distribution. Imbalanced data classification problems present in many application areas like, medical disease diagnosis, risk management, fault-detection, etc. It is a challenging problem in the field of machine learning and data mining. In this paper, K-Means cluster based oversampling algorithm is proposed to solve the imbalanced data classification problem. The experimental results show that the proposed algorithm outperforms the existing oversampling algorithms of previous studies.
CITATION STYLE
Subbulaxmi*, Ms. S. S., & Arumugam, Dr. G. (2020). K-Means Cluster Based Oversampling Algorithm for Imbalanced Data Classification. International Journal of Recent Technology and Engineering (IJRTE), 8(5), 3436–3440. https://doi.org/10.35940/ijrte.e6535.018520
Mendeley helps you to discover research relevant for your work.