Conference Paper/Proceeding/Abstract 313 views
Modelling imbalanced classes
SAS Global Forum 2020 conference proceedings, Pages: 5021 - 2020
Swansea University Author: Desireé Cranfield
In this study separate sampling was applied to various modelling procedures to assist in theidentification of the most important variables describing smartphone users who are securitycompliant. Initial analysis of the data found that only 7% of smartphone users reportedapplying security measures to...
|Published in:||SAS Global Forum 2020 conference proceedings|
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In this study separate sampling was applied to various modelling procedures to assist in theidentification of the most important variables describing smartphone users who are securitycompliant. Initial analysis of the data found that only 7% of smartphone users reportedapplying security measures to protect their phones and/or their personal information storedon their devices.Due to the class imbalance in the target variable, predictive modelling procedures failed toproduce accurate models. Separate sampling proportions were introduced to establish ifclassification accuracy could be improved. This study tested target class over-samplingratios of 20%, 30%, 40% and 50% and compared the results of the models fitted on thesedata sets to those fitted on the original data where no separate sampling was applied.Models fitted included: decision trees, 5-fold cross-validated decision trees, logisticregression, neural networks and gradient boosted decision trees. The results showed thatthe logistic regression and neural network models produced unstable models regardless ofthe target class ratios. More stable models were however reported for the decision trees, 5-fold cross-validated decision trees and gradient boosted decision trees.Variables found to influence mobile security compliance included age, gender and varioussecurity/privacy related behaviors.
School of Management