Abstract
Class imbalance is one of the main problem using different algorithms used in machine learning. In imbalance classification of data the false negative is always high. The researchers have introduced many methods to deal with this problem, but the purpose of this paper is to apply machine learning algorithms under the SMOTE and cost sensitive learning approaches and acquired the results from the different experiments to find out the suitable results for imbalanced data.
Akbar Khan, Faizullah Khan, Surat Khan, Ishtiaq Ahmed Khan, Muhammad Saeed. (2018) Cost Sensitive Learning and SMOTE Methods for Imbalanced Data, Journal of Applied and Emerging Sciences, Volume 8, Issue 1.
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