AN OVERVIEW ON DATA MINING DESIGNED FOR IMBALANCED DATASETS

Mohammad Imran,Dr.Ahmed Abdul Moiz Qyser ,Syed Salman Ali ,Vemulakonda Rajesh

Abstract: The imbalanced datasets with the classifying categories are not around equally characterized. A problem in imbalanced dataset occurs in categorization, where the amount of illustration of single class will be greatly lesser than the illustrations of the previous classes. Current existence brought improved awareness during implementation of machine learning methods to complex real world exertion, which is considered by several through imbalanced data. In machine learning the imbalanced datasets has become a critical problem and also usually found in many implementation such as detection of fraudulent calls, bio-medical, engineering, remote-sensing, computer society and manufacturing industries. In order to overcome the problems several approaches have been proposed. In this paper a study on Imbalanced dataset problem and examine various sampling method utilized in favour of evaluation of the datasets, moreover the interpretation methods are further suitable for imbalanced datasets mining.

Keywords: Imbalance Problems, Imbalanced datasets, sampling strategies, Machine Learning.

DOI: https://doi.org/10.15623/ijret.2014.0310034