Performing Feature Selection Before Removing Outliers To Increase Classfier's Accuracy
Наталія ПЕТЛЯК, Oleksandra Tmenova, Matúš Námešný, Tomáš Bončo, Lubomír Popelínský · 2018
This work addresses the problem of feature selection for boosting the performance of outlier detectors in the context of supervised classification. Different feature selection and outlier detection methods are applied to four datasets used in the experiment and a comparative analysis between combinations of these methods is reported. We present combinations producing the best accuracy of a classifier and show the optimal number of outliers to be removed.