Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 1, Pages 519-526Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.09.052
Keywords
Machine learning; Feed-forward neural network; Data preprocessing; Pattern recognition; Boolean function complexity; Computer-aided design
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Preprocessing the data is an important step while creating neural network (NN) applications because this step usually has a significant effect on the prediction performance of the model. This paper compares different data processing strategies for NNs for prediction of Boolean function complexity (BFC). We compare NNs' predictive capabilities with (1) no preprocessing (2) scaling the values in different curves based on every curve's own peak and then normalizing to [0,1] range (3) applying z-score to values in all curves and then normalizing to [0,1] range, and (4) logarithmically scaling all curves and then normalizing to [0,1] range. The efficiency of these methods was measured by comparing RMS errors in NN-made BFC predictions for numerous ISCAS benchmark circuits. Logarithmic preprocessing method resulted in the best prediction statistics as compared to other techniques. (C) 2007 Elsevier Ltd. All rights reserved.
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