Parameter tuning for 1-class classification with LibSVM in weka

Question

I am doing a 1-class classification with LibSVM wrapper in Weka. But the problem is during TESTING, even if I use the same TRAINING instances, I see most of them are classified as outliers (NaN) which is unreasonable (how this can happen?). If this is something to deal with parameter tuning, what parameters should I try tweaking?

Hi, I am dealing with the same problem. Did you find a solution? — Harsh Patni
im afraid this is being a long time back i don't remember now what i did back then — samsamara

user28636 user28636 · Accepted Answer · 2013-05-07T01:56:16

A classifier needs at least two class values to "work". If all you have is labeled data with one label value(your one class value), then you need to get data that is not part of that class so that a classifier can function.

Parameter tuning for 1-class classification with LibSVM in weka

1 Answers