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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Vydané v:Expert systems with applications Ročník 36; číslo 1; s. 519 - 526
Hlavní autori: Chandana Prasad, P.W., Beg, Azam
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 2009
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2007.09.052