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  NeuroSolutions NeuroSolutions
v2.15NeuroSolutions
for ExcelCustom Solution
WizardInteractive
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Classification
problems often do not have an equal number of training
exemplars (samples) for each class. For example, you may
have a neural network application that detects the
occurrence of cancer from clinical test data. The
training data for this problem may contain 99 exemplars
classified as non-cancerous for every one exemplar
classified as cancerous. A standard neural network would
most often train itself to classify all exemplars as
non-cancerous so that it would be 99% correct. Since the
goal is to detect the existence of cancer, this is a
problem. One way to overcome this problem would be to throw away most of the training exemplars so that there would be an equal number for each class. This would drastically reduce the amount of training data, and likely result in a network with poor generalization. NeuroSolutions provides a better solution using a method called exemplar weighting. For the example above, each of the cancerous training exemplars would have 99 times more weight during the backpropagation procedure than the non cancerous exemplars. This balancing of the training data will most likely result in a system that does a much better job of detecting the cancerous cases. |
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