Trying to find a multiple classifier for known and unknown category outcomes

Hi,

I'm a biologist trying to develop a capture-mark-recapture survey method using animal vocalisations. I've only just started using WEKA. I've formerly used Statistica to run artificial neural networks - MLPs - to show that I can classify vocalisations to individuals with up to 100% accuracy. But if I have new individuals present in the validation database that weren't in the training database, it just classifies them to the nearest individual in the training database. I need it to go to an "unknown" category and have read Probabilistic Neural Networks can do this.

So I have the variables, the correct identity and the knowledge that I can classify them correctly with up to 100% accuracy. What I want is a dual-layer outcome:

1) Identity: known or unknown
and then if identity is known
2) Identity = individual A (confidence level = X)

Can anyone let me know how this would work in WEKA? As I say I'm a novice so as much detail as possible would be wonderful!

Thanks!