dc.contributor.author |
Duma, M
|
|
dc.contributor.author |
Twala, B
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|
dc.contributor.author |
Nelwamondo, Fulufhelo V
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|
dc.contributor.author |
Marwala, T
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|
dc.date.accessioned |
2013-10-23T11:50:33Z |
|
dc.date.available |
2013-10-23T11:50:33Z |
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dc.date.issued |
2013-09 |
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dc.identifier.citation |
Duma, M, Twala, B, Nelwamondo, F.V and Marwala, T. 2013. Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection. Current Science, vol. 103(6), pp 697-705 |
en_US |
dc.identifier.issn |
0011-3891 |
|
dc.identifier.uri |
http://www.currentscience.ac.in/Volumes/103/06/0697.pdf
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|
dc.identifier.uri |
http://hdl.handle.net/10204/6984
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|
dc.description |
Copyright: 2013 Indian Academy of Sciences. This an open access journal. This journal authorizes the publication of the information herewith contained. Published in Current Science, vol. 103(6), pp 697-705 |
en_US |
dc.description.abstract |
We propose a hybrid missing data imputation technique using positive selection and correlation-based feature selection for insurance data. The hybrid is used to help supervised learning methods improve their classification accuracy and resilience in the presence of increasing missing data. The positive selection algorithm searches for potential candidates for imputation and the correlation-based feature selection method searches for attributes have a significant effect on the target outcome. The imputation is performed only on those attributes that have an impact on the target outcome. The results show that the classification accuracy and resilience of supervised learning methods improve significantly when applied with the imputation strategy under these assumptions. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Indian Academy of Sciences |
en_US |
dc.relation.ispartofseries |
Workflow;11509 |
|
dc.subject |
Insurance risk classification |
en_US |
dc.subject |
Missing data |
en_US |
dc.subject |
Positive selection algorithm |
en_US |
dc.subject |
Supervised learning methods |
en_US |
dc.subject |
Insurance data |
en_US |
dc.title |
Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection |
en_US |
dc.type |
Article |
en_US |
dc.identifier.apacitation |
Duma, M., Twala, B., Nelwamondo, F. V., & Marwala, T. (2013). Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection. http://hdl.handle.net/10204/6984 |
en_ZA |
dc.identifier.chicagocitation |
Duma, M, B Twala, Fulufhelo V Nelwamondo, and T Marwala "Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection." (2013) http://hdl.handle.net/10204/6984 |
en_ZA |
dc.identifier.vancouvercitation |
Duma M, Twala B, Nelwamondo FV, Marwala T. Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection. 2013; http://hdl.handle.net/10204/6984. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Duma, M
AU - Twala, B
AU - Nelwamondo, Fulufhelo V
AU - Marwala, T
AB - We propose a hybrid missing data imputation technique using positive selection and correlation-based feature selection for insurance data. The hybrid is used to help supervised learning methods improve their classification accuracy and resilience in the presence of increasing missing data. The positive selection algorithm searches for potential candidates for imputation and the correlation-based feature selection method searches for attributes have a significant effect on the target outcome. The imputation is performed only on those attributes that have an impact on the target outcome. The results show that the classification accuracy and resilience of supervised learning methods improve significantly when applied with the imputation strategy under these assumptions.
DA - 2013-09
DB - ResearchSpace
DP - CSIR
KW - Insurance risk classification
KW - Missing data
KW - Positive selection algorithm
KW - Supervised learning methods
KW - Insurance data
LK - https://researchspace.csir.co.za
PY - 2013
SM - 0011-3891
T1 - Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection
TI - Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection
UR - http://hdl.handle.net/10204/6984
ER -
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en_ZA |