Estimation of Missing Data Using Computational Intelligence and Decision Trees

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

This paper introduces a novel paradigm to impute missing data that combines a decision tree with an auto-associative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimize an error function derived from the respective model. The models' ability to impute missing data is tested and compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13% with the AANN based model's average accuracy increasing from 75.8% to 86.3% while that of the PCA-NN based model increasing from 66.1% to 81.6%.
14 pages

Keywords

Applications

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/137739

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0709.1640
 http://arxiv.org/abs/0709.1640

Collections

arXiv