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dc.contributor.author Duma, M
dc.contributor.author Twala, B
dc.contributor.author Nelwamondo, FV
dc.contributor.author Marwala, T
dc.date.accessioned 2013-10-23T11:50:33Z
dc.date.available 2013-10-23T11:50:33Z
dc.date.issued 2013-09
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
dc.identifier.uri http://hdl.handle.net/10204/6984
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


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