Missing data imputation in multivariate data by evolutionary algorithms
| Autor: | Dusko Kalenatic, César Amilcar López Bello, Juan Carlos Figueroa Garcia |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Multivariate statistics
Multiple data imputation Fitness function business.industry Computer science Covariance matrix Missing data Evolutionary optimization Evolutionary algorithm Pattern recognition computer.software_genre Human-Computer Interaction Error function Multivariate analysis Arts and Humanities (miscellaneous) Genetic algorithm Artificial intelligence Data mining CMA-ES business computer General Psychology |
| Zdroj: | Repositorio Universidad de la Sabana Universidad de la Sabana instacron:Universidad de la Sabana |
| ISSN: | 0747-5632 |
| DOI: | 10.1016/j.chb.2010.06.026 |
| Popis: | 7 páginas This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix and vector of means is presented. All methodological aspects of the genetic structure are presented. An extended explanation of the design of the fitness function is provided. An application example is solved by the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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