Computational intelligence and decision trees for missing data estimation

George Ssali; Tshilidzi Marwala

doi:10.1109/IJCNN.2008.4633790

Computational intelligence and decision trees for missing data estimation

George Ssali
, Tshilidzi Marwala

University of the Witwatersrand

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

This paper introduces a novel paradigm to impute missing data that combines a decision tree with an autoassociative 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 minimise 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%.

Original language	English
Title of host publication	2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages	201-207
Number of pages	7
DOIs	https://doi.org/10.1109/IJCNN.2008.4633790
Publication status	Published - 2008
Externally published	Yes
Event	2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China Duration: 1 Jun 2008 → 8 Jun 2008

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2008 International Joint Conference on Neural Networks, IJCNN 2008
Country/Territory	China
City	Hong Kong
Period	1/06/08 → 8/06/08

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2008.4633790

Cite this

@inproceedings{dca7f6754c4d4db59ae19fa802e9691d,

title = "Computational intelligence and decision trees for missing data estimation",

abstract = "This paper introduces a novel paradigm to impute missing data that combines a decision tree with an autoassociative 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 minimise 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\%.",

author = "George Ssali and Tshilidzi Marwala",

year = "2008",

doi = "10.1109/IJCNN.2008.4633790",

language = "English",

isbn = "9781424418213",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "201--207",

booktitle = "2008 International Joint Conference on Neural Networks, IJCNN 2008",

note = "2008 International Joint Conference on Neural Networks, IJCNN 2008 ; Conference date: 01-06-2008 Through 08-06-2008",

}

Ssali, G & Marwala, T 2008, Computational intelligence and decision trees for missing data estimation. in 2008 International Joint Conference on Neural Networks, IJCNN 2008., 4633790, Proceedings of the International Joint Conference on Neural Networks, pp. 201-207, 2008 International Joint Conference on Neural Networks, IJCNN 2008, Hong Kong, China, 1/06/08. https://doi.org/10.1109/IJCNN.2008.4633790

Computational intelligence and decision trees for missing data estimation. / Ssali, George; Marwala, Tshilidzi.
2008 International Joint Conference on Neural Networks, IJCNN 2008. 2008. p. 201-207 4633790 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computational intelligence and decision trees for missing data estimation

AU - Ssali, George

AU - Marwala, Tshilidzi

PY - 2008

Y1 - 2008

N2 - This paper introduces a novel paradigm to impute missing data that combines a decision tree with an autoassociative 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 minimise 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%.

AB - This paper introduces a novel paradigm to impute missing data that combines a decision tree with an autoassociative 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 minimise 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%.

UR - https://www.scopus.com/pages/publications/56349109493

U2 - 10.1109/IJCNN.2008.4633790

DO - 10.1109/IJCNN.2008.4633790

M3 - Conference contribution

AN - SCOPUS:56349109493

SN - 9781424418213

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 201

EP - 207

BT - 2008 International Joint Conference on Neural Networks, IJCNN 2008

T2 - 2008 International Joint Conference on Neural Networks, IJCNN 2008

Y2 - 1 June 2008 through 8 June 2008

ER -

Computational intelligence and decision trees for missing data estimation

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this