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Title:
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QEA FOR OUTLIER DETECTION IN SIMPLE LINEAR REGRESSION |
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Author(s):
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A. A. M. Nurunnabi , Salena Akter |
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ISBN:
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978-972-8924-97-3 |
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Editors:
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Hans Weghorn and Pedro Isaías |
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Year:
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2009 |
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Edition:
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V II, 2 |
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Keywords:
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Genetic algorithm, linear regression, outlier, QEA, regression diagnostics, and robust regression. |
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Type:
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Short Paper |
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First Page:
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28 |
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Last Page:
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32 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Outlier detection belongs to the most important tasks in data analysis. Regression analysis has seen a great impact on
learning from data. Generally, regression diagnostics and robust regression are employed for outlier detection in
regression analysis. In this paper we propose a quantum-inspired evolutionary algorithm (QEA) for identifying outliers in
simple linear regression. Numerical examples show the success of the new algorithm, and make the comparisons with the
existing diagnostic methods. |
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