A new approach for classifying large number of mixed variables
The issue of classifying objects into one of predefined groups when the measured variables are mixed with different types of variables has been part of interest among statisticians in many years. Some methods for dealing with such situation have been introduced that include parametric, semi-paramet...
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/5194/1/hashibah2.pdf http://repo.uum.edu.my/5194/ http://www.waset.org/journals/waset/v70/v70-32.pdf |
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| Summary: | The issue of classifying objects into one of predefined
groups when the measured variables are mixed with different types of variables has been part of interest among statisticians in many years. Some methods for dealing with such situation have been introduced that include parametric, semi-parametric and non-parametric approaches. This paper attempts to discuss on a problem in classifying a data when the number of measured mixed variables is
larger than the size of the sample.A propose idea that integrates a dimensionality reduction technique via principal component analysis and a discriminant function based on the location model is discussed. The study aims in offering practitioners another potential tool in a classification problem that is possible to be considered when the observed variables are mixed and too large. |
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