This paper describes the application of a genetic algorithm to nearest-neighbour based imputation of sample data into a census data dataset. The genetic algorithm optimises the selection and weights of variables used for measuring distance. The results show that the measure of fit can be improved by selecting imputation variables using a genetic algorithm. The percentage of variance explained in the goal variables increases compared to a simple selection of imputation variables. This quantitative approach to the selection of imputation variables does not deny the importance of expertise. Human expertise is still essential in defining the optional set of imputation variables.
|Title of host publication||Innovations in Applied Artificial Intelligence, 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2005, Bari, Italy, June 22-24, 2005|
|Place of Publication||Berlin Heidelberg|
|Publication status||Published - 2005|
|Event||18 th International Conference, IEA/AIE 2005, Bari (Italy) - |
Duration: 22 Jun 2005 → 24 Jun 2005
|Conference||18 th International Conference, IEA/AIE 2005, Bari (Italy)|
|Period||22/06/05 → 24/06/05|