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...
Gespeichert in:
Veröffentlicht in: | Expert systems with applications 2009, Vol.36 (1), p.519-526 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 526 |
---|---|
container_issue | 1 |
container_start_page | 519 |
container_title | Expert systems with applications |
container_volume | 36 |
creator | Chandana Prasad, P.W. Beg, Azam |
description | 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. |
doi_str_mv | 10.1016/j.eswa.2007.09.052 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_35990817</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0957417407004691</els_id><sourcerecordid>35990817</sourcerecordid><originalsourceid>FETCH-LOGICAL-c331t-3c36e8d47bd401edcc832544b997ecce24a83a64bf979efd4dcee3f45642faca3</originalsourceid><addsrcrecordid>eNp9kLFOwzAQhi0EEqXwAkyZ2BLs2IljiQVVFCpVYoHZcs-X4iqJg-0WeHtSlZnppNP_nf77CLlltGCU1fe7AuOXKUpKZUFVQavyjMxYI3leS8XPyYyqSuaCSXFJrmLcUcrklJ2R5Wo4YExua5Ibtpk1yWRjwDF4wBiPqx7Th7cxa33IwAXYu5SB78cOv136yXpvsYvX5KI1XcSbvzkn78unt8VLvn59Xi0e1zlwzlLOgdfYWCE3VlCGFqDhZSXERimJAFgK03BTi02rpMLWCguIvBVVLcrWgOFzcne6OxX83E_Fde8iYNeZAf0-al4pRRsmp2B5CkLwMQZs9Rhcb8KPZlQflemdPirTR2WaKj0pm6CHEzR9hAeHQUdwOABaFxCStt79h_8CVnN3eA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>35990817</pqid></control><display><type>article</type><title>Investigating data preprocessing methods for circuit complexity models</title><source>ScienceDirect Journals (5 years ago - present)</source><creator>Chandana Prasad, P.W. ; Beg, Azam</creator><creatorcontrib>Chandana Prasad, P.W. ; Beg, Azam</creatorcontrib><description>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.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2007.09.052</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Boolean function complexity ; Computer-aided design ; Data preprocessing ; Feed-forward neural network ; Machine learning ; Pattern recognition</subject><ispartof>Expert systems with applications, 2009, Vol.36 (1), p.519-526</ispartof><rights>2007 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-3c36e8d47bd401edcc832544b997ecce24a83a64bf979efd4dcee3f45642faca3</citedby><cites>FETCH-LOGICAL-c331t-3c36e8d47bd401edcc832544b997ecce24a83a64bf979efd4dcee3f45642faca3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eswa.2007.09.052$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,778,782,3539,4012,27910,27911,27912,45982</link.rule.ids></links><search><creatorcontrib>Chandana Prasad, P.W.</creatorcontrib><creatorcontrib>Beg, Azam</creatorcontrib><title>Investigating data preprocessing methods for circuit complexity models</title><title>Expert systems with applications</title><description>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.</description><subject>Boolean function complexity</subject><subject>Computer-aided design</subject><subject>Data preprocessing</subject><subject>Feed-forward neural network</subject><subject>Machine learning</subject><subject>Pattern recognition</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNp9kLFOwzAQhi0EEqXwAkyZ2BLs2IljiQVVFCpVYoHZcs-X4iqJg-0WeHtSlZnppNP_nf77CLlltGCU1fe7AuOXKUpKZUFVQavyjMxYI3leS8XPyYyqSuaCSXFJrmLcUcrklJ2R5Wo4YExua5Ibtpk1yWRjwDF4wBiPqx7Th7cxa33IwAXYu5SB78cOv136yXpvsYvX5KI1XcSbvzkn78unt8VLvn59Xi0e1zlwzlLOgdfYWCE3VlCGFqDhZSXERimJAFgK03BTi02rpMLWCguIvBVVLcrWgOFzcne6OxX83E_Fde8iYNeZAf0-al4pRRsmp2B5CkLwMQZs9Rhcb8KPZlQflemdPirTR2WaKj0pm6CHEzR9hAeHQUdwOABaFxCStt79h_8CVnN3eA</recordid><startdate>2009</startdate><enddate>2009</enddate><creator>Chandana Prasad, P.W.</creator><creator>Beg, Azam</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>2009</creationdate><title>Investigating data preprocessing methods for circuit complexity models</title><author>Chandana Prasad, P.W. ; Beg, Azam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-3c36e8d47bd401edcc832544b997ecce24a83a64bf979efd4dcee3f45642faca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Boolean function complexity</topic><topic>Computer-aided design</topic><topic>Data preprocessing</topic><topic>Feed-forward neural network</topic><topic>Machine learning</topic><topic>Pattern recognition</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chandana Prasad, P.W.</creatorcontrib><creatorcontrib>Beg, Azam</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chandana Prasad, P.W.</au><au>Beg, Azam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Investigating data preprocessing methods for circuit complexity models</atitle><jtitle>Expert systems with applications</jtitle><date>2009</date><risdate>2009</risdate><volume>36</volume><issue>1</issue><spage>519</spage><epage>526</epage><pages>519-526</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><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.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2007.09.052</doi><tpages>8</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0957-4174 |
ispartof | Expert systems with applications, 2009, Vol.36 (1), p.519-526 |
issn | 0957-4174 1873-6793 |
language | eng |
recordid | cdi_proquest_miscellaneous_35990817 |
source | ScienceDirect Journals (5 years ago - present) |
subjects | Boolean function complexity Computer-aided design Data preprocessing Feed-forward neural network Machine learning Pattern recognition |
title | Investigating data preprocessing methods for circuit complexity models |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T14%3A06%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Investigating%20data%20preprocessing%20methods%20for%20circuit%20complexity%20models&rft.jtitle=Expert%20systems%20with%20applications&rft.au=Chandana%20Prasad,%20P.W.&rft.date=2009&rft.volume=36&rft.issue=1&rft.spage=519&rft.epage=526&rft.pages=519-526&rft.issn=0957-4174&rft.eissn=1873-6793&rft_id=info:doi/10.1016/j.eswa.2007.09.052&rft_dat=%3Cproquest_cross%3E35990817%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=35990817&rft_id=info:pmid/&rft_els_id=S0957417407004691&rfr_iscdi=true |