Investigating data preprocessing methods for circuit complexity models

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 (B...

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Veröffentlicht in:Expert systems with applications Jg. 36; H. 1; S. 519 - 526
Hauptverfasser: Chandana Prasad, P.W., Beg, Azam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 2009
Schlagworte:
Boolean function complexity
Computer-aided design
Data preprocessing
Feed-forward neural network
Machine learning
Pattern recognition
Feed-forward neural network
Pattern recognition
Computer-aided design
Data preprocessing
Boolean function complexity
Machine learning
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2007.09.052