Estimation of Missing Data Using Computational Intelligence and Decision Trees
| dc.creator | Ssali, George | |
| dc.creator | Marwala, Tshilidzi | |
| dc.date | 2007-09-11 | |
| dc.date.accessioned | 2026-07-07T08:28:46Z | |
| dc.date.available | 2026-07-07T08:28:46Z | |
| dc.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%. | |
| dc.description | 14 pages | |
| dc.identifier | https://arxiv.org/abs/0709.1640 | |
| dc.identifier | http://arxiv.org/abs/0709.1640 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/137739 | |
| dc.subject | Applications | |
| dc.title | Estimation of Missing Data Using Computational Intelligence and Decision Trees | |
| dc.type | text |