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Classifier scoring system

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Hello,

 

I am trying to use the custom classfier VI included in the Vision Development Module in 2010. I classifying three different types of images A, B and C and have created different type of feature fectors for them using different momentums and image processing techniques. However, I am able to get a separtaion for the three classes but I find it very confusing the way the classifier is scored. I need to understand it so that I can compare the different feature vectors that I have created and pick the best one. I have tried SVM and Nearest Neighbor classifiers and I also want to compare their performance.

 

Using the IMAQ Classifier Accuracy VI I am able to get some scores out. But I would appreciate if someone could explain more deeply the theoretical meaning of these scores:

- Class Predictive Value

- Classification Distribution

- Class Distance Table

- Accuracy

 

Thanks a lot!

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Here is the help file on that.

 

Class Predictive Value;

Proportion of samples identified correctly per class.

 

Classification Distribution;

Table of classification results. Is this what you mean by class distance table?

 

Accuracy;

Proportion of samples classified correctly out of all samples.

 

If you have more questions, let me know. Hope that helps!

John B.
Embedded Networks R&D
National Instruments
Certified LabVIEW Developer
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