I have a classification dataset with 148 input (independent) features, most of which are expected beforehand to be irrelevant. So, at the moment, I am using feature selection methods to discard the irrelevant features before removing outliers.
From one perspective, this seems to be a good idea since an outlier in one feature space may not be so in a different (reduced) feature space. On the other hand, however, the presence of the outliers in the dataset, on which the feature selection methods are applied, may negatively influence the selected features.
The feature selection methods I am using are information gain, gain ratio, symmetrical uncertainty, fisher score and gini index.
The question is: what is the "most" appropriate order of applying these two dataset preprocessing steps?