How does feature selection impact outlier detection and also, removing outliers impact feature selection? It could be a basic question. However, just to know the boundaries, I asked. Thanks in advance. I have gone through the following:Feature selection and outlier order

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    $\begingroup$ Is there any specific thing you want to ask because Feature selection and outlier order already answer the question? $\endgroup$ – prashant0598 Aug 1 at 15:19
  • $\begingroup$ Hi @prashant0598 , thanks for answering. Like if we are working with imbalanced data , and I need to do feature selection. but finding the minority which is behaving like outlier, feature selection wont be a good idea then. ? $\endgroup$ – Payal Bhatia Aug 12 at 14:58

Outlier detection is part of data preprocessing and used to remove some of the rare events but it could happen that rare events are important to us like fraud detection in that case it becomes important and so we can't do outlier detection beforehand.

In that case we do various approaches like undersampling of majority events or oversampling of minority class. For various approaches refer this

You could also look into KPCA. Having said that there is no particular solution it all depends upon your dataset.

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