All,
I am using a neural network for some classification/pattern recognition and would like to punish the system performance greater for false alarms than for plain misses. After looking through the documentation, it looks like I may be able to accomplish this by using a weighted sse for the performance function. The documentation on using the weights is quite poor.
1.) Does anyone have an example of how to use a weighted sse as a performance function?
2.) Is there a better way to get the system to minimize the number of false alarms?
The original problem is that the data being analyzed has many more non-events versus events, so frequently there are more false alarms than hits. When training, I present an equal number of events and non events, however this still results in many false alarms when the entire dataset is analyzed after training. Suggestions?
Things I've done:
- Normalized input to the network
- Presented equal numbers of events/non-events during training
- Applied a PCA to eliminate correlation in input
Is this just a sign that more training needs to be done? Any insight would be greatly appreciated!
Thanks!
-Eric
I apologize if this posted twice, my first attempt did not appear to work.
