Title :
A new feedforward neural network pruning algorithm: SSM-iterative pruning (SSMIP)
Author :
Fnaiech, Nader ; Fnaiech, Farhat ; Cheriet, Mohamed
Author_Institution :
CEREP, Ecole Superieure des Sci. et Techniques, Tunis, Tunisia
Abstract :
We present a new pruning algorithm based on a combination of the Statistical Stepwise Method (SSM) (Cottrell et al., 1995) and the Iterative Pruning (IP) algorithms (Castellano and Fanelli, 1997). The proposed algorithm (SSMIP) is used to prune feedforward neural networks (NN) and combines the advantages of the algorithms. The new proposed pruning algorithm works as follows: i) The Iterative Pruning Algorithm of Castellano et al. is applied in order to cancel no significant units; ii) The Statistical Stepwise Method of Cottrell et al. is then used to remove unnecessary links between units. These steps are repeated layer by layer in the forward direction with holding the previous optimised structure in each layer. Simulation results are given on standard benchmarks that highlight the effectiveness of the proposed algorithm. A comparative study is given with respect to Castelanno and Cottrell algorithms used separately.
Keywords :
feedforward neural nets; learning (artificial intelligence); multilayer perceptrons; pattern recognition; Iterative Pruning; SSM-iterative pruning; Statistical Stepwise Method; benchmarks; feedforward neural network; pattern recognition; pruning algorithm; simulation; Artificial intelligence; Artificial neural networks; Feedforward neural networks; Feedforward systems; Iterative algorithms; Iterative methods; Laboratories; Neural networks; Pattern recognition; Statistical analysis;
Conference_Titel :
Systems, Man and Cybernetics, 2002 IEEE International Conference on
Print_ISBN :
0-7803-7437-1
DOI :
10.1109/ICSMC.2002.1173310