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Eitrich and Lang ,2006[2] have proposed an approach to efficiently optimize machine learning parameters of support vectors for unbalanced data sets.
Pozzolo et al. 2015[3] uses Bayes minimum risk theory to find the correct classification threshold and show how to adjust it after downsampling. From imbalanced data sets, the objective functions used to learn classifiers generally tend to favor larger and less important classes.
Amalia et al,2019[4] perform an impact analysis on the performance of unbalanced datasets
but focusing on the effects of data resampling.
Johnson and Khoshgoftaar 2019 [5]did a survey that highlights various gaps in deep learning from imbalanced data.
We are not aware of any work analyzing the impact of imbalanced datasets on machine learning models for detecting malicious android applications. That justifies our contribution.
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IEEE/ICACT20220050 Slide.04
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