Missing Data: A Comparison of Neural Network and Expectation
Maximisation Techniques
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Missing Data: A Comparison of Neural Network and Expectation
Maximisation Techniques
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| Id. |
25597799 |
| Titulo |
Missing Data: A Comparison of Neural Network and Expectation
Maximisation Techniques |
| Autor(es) |
Nelwamondo, Fulufhelo V.
Mohamed, Shakir
Marwala, Tshilidzi
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| Localización |
http://arxiv.org/abs/0704.3474
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| Versión |
1.0 |
| Estado |
Final
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| Descripción |
The estimation of missing input vector elements in real time processing
applications requires a system that possesses the knowledge of certain
characteristics such as correlations between variables, which are inherent in
the input space. Computational intelligence techniques and maximum likelihood
techniques do possess such characteristics and as a result are important for
imputation of missing data. This paper compares two approaches to the problem
of missing data estimation. The first technique is based on the current state
of the art approach to this problem, that being the use of Maximum Likelihood
(ML) and Expectation Maximisation (EM. The second approach is the use of a
system based on auto-associative neural networks and the Genetic Algorithm as
discussed by Adbella and Marwala3. The estimation ability of both of these
techniques is compared, based on three datasets and conclusions are made.
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| Palabras clave |
Statistics - Applications
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| Tipo de recurso |
Texto Narrativo
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| Tipo de Interactividad |
Expositivo
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| Nivel de Interactividad |
muy bajo
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| Audiencia |
Estudiante
Profesor
Autor
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| Estructura |
Atomic |
| Coste |
no
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| Copyright |
sí
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| Requerimientos técnicos |
Browser: Any
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| Fecha de contribución |
25-jun-2007 |
| Contacto |
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