• Kolluru VenkataNagendra
• Dr.M.Ussnaiah

The problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the application domains, the state-of-the-art technologies and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. This paper highlights the major opportunities and challenges for learning from imbalanced data.

Classification, Class imbalanced problem, cost-sensitive learning, active learning, assessment metrics

"STATE-OF-ART OF IMBALANCED DATA CLASSIFICATION METHODS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.352-358, December-2018, Available :http://www.jetir.org/papers/JETIR1812251.pdf

"STATE-OF-ART OF IMBALANCED DATA CLASSIFICATION METHODS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp352-358, December-2018, Available at : http://www.jetir.org/papers/JETIR1812251.pdf