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 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
2009
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | 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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| AbstractList | 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. 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. |
| Author | Chandana Prasad, P.W. Beg, Azam |
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| CitedBy_id | crossref_primary_10_1016_j_chemolab_2020_103978 crossref_primary_10_1016_j_eswa_2010_01_002 crossref_primary_10_1016_j_jretconser_2020_102312 crossref_primary_10_1080_0951192X_2011_645381 crossref_primary_10_1016_j_sandf_2014_02_013 crossref_primary_10_1002_cae_20243 crossref_primary_10_1007_s11356_014_2842_7 |
| Cites_doi | 10.1109/6.576011 10.1109/ICCD.2002.1106793 10.1007/11494669_1 10.1016/j.eswa.2007.04.010 10.1109/ANNES.1993.323086 10.1016/S0893-6080(05)80093-0 10.1109/IJCNN.2004.1380065 10.1007/978-3-540-30227-8_30 10.1109/12.73590 10.1016/j.vlsi.2005.06.002 10.1109/TC.1978.1675141 10.1016/j.neucom.2006.01.025 10.1109/54.785838 10.1145/1057661.1057689 10.1109/LPE.1996.547534 |
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| Keywords | Feed-forward neural network Pattern recognition Computer-aided design Data preprocessing Boolean function complexity Machine learning |
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