Classification with missing data using multi-layered artificial immune systems

Mlungisi Duma, Bhekisipho Twala, Tshilidzi Marwala, Fulufhelo Vincent Nelwamondo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The nature of missing data problems forces us to build models that maintain high accuracies and steadiness. The models developed to achieve this are usually complex and computationally expensive. In this paper, we propose an unsupervised multi-layered artificial immune system for an insurance classification problem that is characterised as highly dimensional and contains escalating missing data. The system is compared with the k-nearest neighbour, support vector machines and logistic discriminant models. Overall, the results show that whilst k-nearest neighbour achieves the highest accuracy, the multi-layered artificial immune system is steady and maintains high performance compared to other models, regardless of how the missing data is distributed in a dataset.

Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
Publication statusPublished - 2012
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

Name2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • insurance risk classification
  • missing dat
  • multi-layered artificial immune system

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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