Comparison of methods for outlier detection and their effects on the classification results for a particular data base |
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Authors: | M.J. Gomez Z. De Benzo C. Gomez E. Marcano H. Torres M. Ramirez |
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Affiliation: | Laboratory of Analytical Chemistry, I.V.I.C., Apdo 21827, Caracas 1020-A Venezuela;Department of Data Processing; I.U.T.-R.C., Caracas Venezuela;Escuela Luis Razetti, U.C.V., Caracas Venezuela |
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Abstract: | One of the drawbacks for using linear discriminant analysis (LDA) is the presence of outliers. Some methods of detecting outliers are compared and applied to a particular data base. When multivariate methods (multinormal distribution procedure and Hawkins' procedure) were applied, the two subsets produced did not differ greatly. Assumptions needed for the application of LDA were evaluated for each subset. Classification ability, feature selection and prediction ability were considered for each subset. Results for each subset were quite different. Hawkins' procedure seems the better method for detecting outliers. |
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Keywords: | Linear discriminant analysis |
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