A comparison of three class separability measures

 

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dc.contributor.author Mthembu, N S
dc.contributor.author Greene, J R
dc.date.accessioned 2017-04-04T09:33:20Z
dc.date.available 2017-04-04T09:33:20Z
dc.date.issued 2004
dc.identifier.citation Mthembu, NS ;Greene, JR. (2004). A comparison of three class separability measures, 63-67
dc.identifier.uri http://hdl.handle.net/11427/24145
dc.description.abstract Measures of class separability can provide valuable insights into data, and suggest promising classification algorithms and approaches in data mining. We compare three simple class separability measures used in supervised machine learning. Their relative effectiveness is evaluated through their functional relationships and their random projections of data onto R 2 for visualization. We conclude that the simple direct class separability measure of a dataset is an easier and more informative measure for separability than the class scatter matrices approach and it correlates well with Thornton’s Separability’s index.
dc.language.iso eng
dc.title A comparison of three class separability measures
dc.type Other
dc.date.updated 2016-01-07T09:12:02Z
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Engineering and the Built Environment
dc.publisher.department Department of Electrical Engineering en_ZA
uct.type.filetype
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation 2004. <i>A comparison of three class separability measures.</i> http://hdl.handle.net/11427/24145 en_ZA
dc.identifier.chicagocitation . 2004. <i>A comparison of three class separability measures.</i> http://hdl.handle.net/11427/24145 en_ZA
dc.identifier.vancouvercitation . 2004. <i>A comparison of three class separability measures.</i> http://hdl.handle.net/11427/24145 en_ZA
dc.identifier.ris TY - AU - Mthembu, N S AU - Greene, J R AB - Measures of class separability can provide valuable insights into data, and suggest promising classification algorithms and approaches in data mining. We compare three simple class separability measures used in supervised machine learning. Their relative effectiveness is evaluated through their functional relationships and their random projections of data onto R 2 for visualization. We conclude that the simple direct class separability measure of a dataset is an easier and more informative measure for separability than the class scatter matrices approach and it correlates well with Thornton’s Separability’s index. DA - 2004 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2004 T1 - A comparison of three class separability measures TI - A comparison of three class separability measures UR - http://hdl.handle.net/11427/24145 ER - en_ZA


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