I designed a neural network for binary classification in
What are differences between two classes? How system detects a sample is from class 1 or 2? For example some if,then functions or some ranges. Something which can help user to have clearer view of the results. For example neural network found that a sample is from class 1. I should say in report that what are differences between in this sample and other samples (class 2) for this results (based on neural network inputs). I know this is a black box but I need more results. I'm checking the trained system using out-of-sample data.
I want know the effects of inputs on output. Which input (feature) is more impotent (has higher weight) on output of trained neural network.
PS. Suppose that i have 5 inputs (features) that i checked we have higher accuracies with these input's combination. So now i want find class 1 and class 2 characteristics. My neural network has two hidden layers. First one has 5 neurons and second one has 3 neurons. My hidden layers transfer function is 'tansig' and output transfer function is 'softmax' for reporting probabilities for outputs. Now what should i do ?