classification by backpropagation
Multi-Layer Perceptron
Network Pruning and Rule Extraction
•Network pruning
–Fully connected network will be hard to articulate
–N input nodes, h hidden nodes and m output nodes lead to h(m+N) weights
–Pruning: Remove some of the links without affecting classification accuracy of the network
•Extracting rules from a trained network
–Discretize activation values; replace individual activation value by the cluster average maintaining the network accuracy
–Enumerate the output from the discretized activation values to find rules between activation value and output
–Find the relationship between the input and activation value
–Combine the above two to have rules relating the output to input
•Perform sensitivity analysis
–Assess the impact of a given input variable on the output