Do we need to normalize the Data in outlier detection?
Hello All,
For finding the outliers, Do we need to normalize the Data?
Can you please tell me impact without normalizing?
Which methods are best for Normalized data for Outlier detection?
And Which methods are better if I don't want to normalize?
Thank you in advance
Yours
Anki
For finding the outliers, Do we need to normalize the Data?
Can you please tell me impact without normalizing?
Which methods are best for Normalized data for Outlier detection?
And Which methods are better if I don't want to normalize?
Thank you in advance
Yours
Anki
0
Answers
If you have a single dimension with a much larger scale than the other dimensions, this single dimension might overrule the others.
Totally depends, there should probably also be a No-Free-Lunch-Theorem for outlier detection
In general, I have good experiences with the local outlier factor method.
Don't do this (see above).
Cheers,
Ingo
Thank you very much.
Yours
Anki
I would like to add:
Visualization. Finding a way to nicely visualize your data can be time consuming but should be worth it.
You can often spot outliers yourself.
You can also look at misclassifications. The ada-boost algorithm adds weight to instances that are misclassified.
Outliers are often given a very high weight after only a few iterations.
Best regards,
Wessel