Applying feature selective validation (FSV) as an objective function for data optimization
Feature Select Validation (FSV) is a widely used validation method for data comparison. FSV provides a quantitative standard to describe the similarity between two sets of data. In this paper, the application of the FSV technique is extended to data optimization. The raw data obtained from simulatio...
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| creator | Siming Pan Hanfeng Wang Jun Fan |
| description | Feature Select Validation (FSV) is a widely used validation method for data comparison. FSV provides a quantitative standard to describe the similarity between two sets of data. In this paper, the application of the FSV technique is extended to data optimization. The raw data obtained from simulations or measurements are often non-ideal for further processing. Several techniques, such as data perturbation, can be used to improve the data quality in certain aspects. However, after modifications the new data could be very different to the original one. Using FSV as an objective function for the optimization process is discussed in this paper, in an example of causality enforcement, to ensure the enforced casual data has the minimum deviations from the original data. The results demonstrate that the proposed approach in this paper is effective for data modification and optimization. |
| doi_str_mv | 10.1109/ISEMC.2010.5711366 |
| format | Conference Proceeding |
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FSV provides a quantitative standard to describe the similarity between two sets of data. In this paper, the application of the FSV technique is extended to data optimization. The raw data obtained from simulations or measurements are often non-ideal for further processing. Several techniques, such as data perturbation, can be used to improve the data quality in certain aspects. However, after modifications the new data could be very different to the original one. Using FSV as an objective function for the optimization process is discussed in this paper, in an example of causality enforcement, to ensure the enforced casual data has the minimum deviations from the original data. 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FSV provides a quantitative standard to describe the similarity between two sets of data. In this paper, the application of the FSV technique is extended to data optimization. The raw data obtained from simulations or measurements are often non-ideal for further processing. Several techniques, such as data perturbation, can be used to improve the data quality in certain aspects. However, after modifications the new data could be very different to the original one. Using FSV as an objective function for the optimization process is discussed in this paper, in an example of causality enforcement, to ensure the enforced casual data has the minimum deviations from the original data. The results demonstrate that the proposed approach in this paper is effective for data modification and optimization.</description><subject>causality check</subject><subject>causality enforcement</subject><subject>data optimization</subject><subject>data perturbation</subject><subject>Feature selective validation (FSV)</subject><subject>Gallium</subject><subject>Interpolation</subject><subject>Numerical models</subject><subject>Optimization</subject><subject>Polynomials</subject><subject>Scattering parameters</subject><subject>Transforms</subject><issn>2158-110X</issn><issn>2158-1118</issn><isbn>9781424463053</isbn><isbn>142446305X</isbn><isbn>9781424463077</isbn><isbn>1424463084</isbn><isbn>1424463076</isbn><isbn>9781424463084</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkE1Lw0AQhtePgqXmD-hlj3pI3dnZ3UmPpbRaqHioingpm81GtqRJSNJC_fWWWgTfy_DyPDOHYewGxBBAjB7my-nzZCjFoWsCQGPOWDSiBJRUyqAgOmd9CTqJASC5-Mc0Xv4x8dFj_QRjo7RSoysWte1aHKIlIUCffY7rutiH8ovn3nbbxvPWF951Yef5zhYhs12oSn43W77fc9tyW_IqXZ-EfFu6I86rhh9My6u6C5vwfVy6Zr3cFq2PTnPA3mbT18lTvHh5nE_GizgA6S4mFF4KSylQJmRCuVMuN5oy7byx6IxWggw4BxkRSkilRS8SpTJUiAJwwG5_7wbv_apuwsY2-9Xpa_gDP3xZmg</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Siming Pan</creator><creator>Hanfeng Wang</creator><creator>Jun Fan</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201007</creationdate><title>Applying feature selective validation (FSV) as an objective function for data optimization</title><author>Siming Pan ; Hanfeng Wang ; Jun Fan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-730e20a7b17d0287fc4cf657d5ce6a3c6540761cc1d77321b2a3e0844d3433013</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>causality check</topic><topic>causality enforcement</topic><topic>data optimization</topic><topic>data perturbation</topic><topic>Feature selective validation (FSV)</topic><topic>Gallium</topic><topic>Interpolation</topic><topic>Numerical models</topic><topic>Optimization</topic><topic>Polynomials</topic><topic>Scattering parameters</topic><topic>Transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Siming Pan</creatorcontrib><creatorcontrib>Hanfeng Wang</creatorcontrib><creatorcontrib>Jun Fan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Siming Pan</au><au>Hanfeng Wang</au><au>Jun Fan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Applying feature selective validation (FSV) as an objective function for data optimization</atitle><btitle>2010 IEEE International Symposium on Electromagnetic Compatibility</btitle><stitle>ISEMC</stitle><date>2010-07</date><risdate>2010</risdate><spage>718</spage><epage>721</epage><pages>718-721</pages><issn>2158-110X</issn><eissn>2158-1118</eissn><isbn>9781424463053</isbn><isbn>142446305X</isbn><eisbn>9781424463077</eisbn><eisbn>1424463084</eisbn><eisbn>1424463076</eisbn><eisbn>9781424463084</eisbn><abstract>Feature Select Validation (FSV) is a widely used validation method for data comparison. FSV provides a quantitative standard to describe the similarity between two sets of data. In this paper, the application of the FSV technique is extended to data optimization. The raw data obtained from simulations or measurements are often non-ideal for further processing. Several techniques, such as data perturbation, can be used to improve the data quality in certain aspects. However, after modifications the new data could be very different to the original one. Using FSV as an objective function for the optimization process is discussed in this paper, in an example of causality enforcement, to ensure the enforced casual data has the minimum deviations from the original data. The results demonstrate that the proposed approach in this paper is effective for data modification and optimization.</abstract><pub>IEEE</pub><doi>10.1109/ISEMC.2010.5711366</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | causality check causality enforcement data optimization data perturbation Feature selective validation (FSV) Gallium Interpolation Numerical models Optimization Polynomials Scattering parameters Transforms |
| title | Applying feature selective validation (FSV) as an objective function for data optimization |
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