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Thread Subject:
Performance function with pattern recognition in neural networks

Subject: Performance function with pattern recognition in neural networks

From: William

Date: 27 Mar, 2013 19:17:22

Message: 1 of 2

Hi,

A question for anyone who might know or have an opinion.

I have set up a neural network to perform a pattern recognition (or classification) but I have found I am getting way too many false negatives compared to what I might actually get with say a Support Vector Machine set up. One possibility I am thinking is that the SVM set up can have harsh penalties for incorrect classifications. So, with this in mind, is there a "best" performance function for pattern recognition with neural networks?? Or am I best to say use use some function on the distance from the hyperplane (or similar)??

Cheers

Subject: Performance function with pattern recognition in neural networks

From: Greg Heath

Date: 29 Mar, 2013 22:09:07

Message: 2 of 2

"William " <william.henson@amec.com> wrote in message <kivgk2$i7$1@newscl01ah.mathworks.com>...
> Hi,
>
> A question for anyone who might know or have an opinion.
>
> I have set up a neural network to perform a pattern recognition (or classification) but I have found I am getting way too many false negatives compared to what I might actually get with say a Support Vector Machine set up. One possibility I am thinking is that the SVM set up can have harsh penalties for incorrect classifications. So, with this in mind, is there a "best" performance function for pattern recognition with neural networks?? Or am I best to say use use some function on the distance from the hyperplane (or similar)??
>
> Cheers

You have given absolutely no information that will let any one help you.
Are you using patternnet with tansig/logsig or tansig/softmax ?
Dimension of inputs? How many classes? For c classes does your target
contain columns of the c-dimensional unit matrix eye(c) or eye(c-1)?
How unbalanced is the data set: How large is each class? Are the ratios
of class sizes the same as the apriori probabilities of the general population?
Are the misclassification costs specified or the usual default values {0,1}?

My apriori advice is to standardize your inputs and remove or modify
outliers. Then use duplicates with or without added noise so that the
number in each class is equal. If you have c classes, the c-dimensional
targets and class indices can be obtained from each other via ind2vec
and vec2ind.

Once the net is trained to yield approximately equal errors, you can
transform the outputs by multiplying to account for differences in
class priors and classification costs.

You might find some old posts of mine in comp.ai.neural-nets and
CSSM regarding priors and classification costs that will help.

Hope tis helps.

Greg

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