Classification affected by a lot of outliers in features? How do you deal with outliers? I  am working on a classification problem and I found my data having  a lot of outliers which has resulted in reduction in my recognition rate. I have tried rescaling, normalization techniques like min max, box cox and even log transformation. 
I am considering of eliminating outliers from box plots but I am afraid I might be eliminating useful features/data required to define the model.

Are there any suggestion on how to deal with such cases.
Also further analysis of data revealed that my data constitutes of features belong to dfferent process like web application, apps. I segregated the data based on the processes and I  do see that large variation of process  resulted in different accuracy ranging from 60-95%

Any tips on how to deal with such cases? In the end I want my classifier to classify irrespective of the process type. So with my current issue, does this imply that my features defined are not good enough or is there something else I can do?
If the number of outliers is small and you are concerned that they will destabilize your solution, you could attempt a random forest classifier. The RF fits trees to random selections of data and variables, and collects "votes" from each, thus reducing the impact of outlier valuers.

On the other hand, if the number of outliers is fairly substantital, you might want to create a new class called "outlier". In the training set, apply this label to those values you have deemed to be outliers and then fit the model with the augmented class. Check if the model correctly identifies outliers in the test set. 

This is equivalent to removing the outliers, only it creates a repeatable and machine learning way of doing so.