Please use this identifier to cite or link to this item:
|Title:||Extracting rules from neural networks by pruning and hidden-unit splitting|
|Source:||Setiono, R. (1997-01-01). Extracting rules from neural networks by pruning and hidden-unit splitting. Neural Computation 9 (1) : 205-225. ScholarBank@NUS Repository.|
|Abstract:||An algorithm for extracting rules from a standard three-layer feedforward neural network is proposed. The trained network is first pruned not only to remove redundant connections in the network but, more important, to detect the relevant inputs. The algorithm generates rules from the pruned network by considering only a small number of activation values at the hidden units. If the number of inputs connected to a hidden unit is sufficiently small, then rules that describe how each of its activation values is obtained can be readily generated. Otherwise the hidden unit will be split and treated as output units, with each output unit corresponding to an activation value. A hidden layer is inserted and a new subnetwork is formed, trained, and pruned. This process is repeated until every hidden unit in the network has a relatively small number of input units connected to it. Examples on how the proposed algorithm works are shown using real-world data arising from molecular biology and signal processing. Our results show that for these complex problems, the algorithm can extract reasonably compact rule sets that have high predictive accuracy rates.|
|Source Title:||Neural Computation|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 16, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.