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15.23. "None of the Above" Category

Applies to: NeuralTools, all releases

Can a neural net decide that a case does not belong to any of the categories found in the training data?

Yes, we can interpret the output of a Probabilistic Neural Network in this manner. (PNNs are the default type of net used for category prediction.)

Let's say we train a net to determine the vehicle type, based on some characteristics of a satellite image of a vehicle. In our training data set we have images for which we know the actual type of vehicle: sedan, station wagon, or van. When we ask a PNN to assign an image to one of these categories, we obtain 3 probabilities as our output, adding up to 100%. For example, the probability of a sedan may be 70%, a station wagon 20%, and a van 10%. In this case NeuralTools declares that we have an image of a sedan, with 70% probability (selecting the category with the highest probability).

When making a prediction for another image, it may turn out that none of the probabilities are very high. For example, our output may specify that the probability of a sedan is 30%, a station wagon 40%, and a van 30%. In this case NeuralTools will declare that the image is one of a station wagon. However, the user may choose to interpret the probabilities differently. We may decide that if none of the probabilities exceeds 50%, we treat the item as belonging to some category not found in the training data. This approach makes sense if we know that the list of categories in the training data is incomplete, as it is in our example: for example, the vehicle may be a truck.

To implement this approach to the interpretation of output probabilities, you need to specify that all the probabilities should be included in the Detailed Report. Open the Application Settings dialog, click in Columns in Detailed Reports row, and the dialog "NeuralTools – Columns to Display in Detailed Reports" will be shown; select the check box in the "Probabilities of All Categories (PNN)" row, in the "Prediction" column.

Additional keywords: Unknown category

Last edited: 2014-04-10

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