Reliable classification of childhood acute leukaemia from gene expression data using confidence machines

Tony Bellotti, Zhiyuan Luo, Alex Gammerman

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

17 Citations (Scopus)

Abstract

The Confidence Machine is a recently developed algorithmic framework for making reliable decisions in the face of uncertainty. Control of predictive accuracy is achieved by allowing hedged predictions, with the possible sacrifice of precision. We use the Support Vector Machine learning algorithm to derive a decision rule for the classification of childhood acute leukaemia subtypes from a small training set of gene expression data. We then implement a Confidence Machine for the decision rule and test on an independent data set to demonstrate its error calibration properties. We show that the Confidence Machine can be used to derive reliable predictions, with control of the risk of error whilst maintaining the level of accuracy given by the Support Vector Machine, yielding useful and precise predictions of leukaemia subtypes. Predictions are reliable even in the context of training from small sample size.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Granular Computing
Pages148-153
Number of pages6
Publication statusPublished - 2006
Externally publishedYes
Event2006 IEEE International Conference on Granular Computing - Atlanta, GA, United States
Duration: 10 May 200612 May 2006

Publication series

Name2006 IEEE International Conference on Granular Computing

Conference

Conference2006 IEEE International Conference on Granular Computing
Country/TerritoryUnited States
CityAtlanta, GA
Period10/05/0612/05/06

ASJC Scopus subject areas

  • General Engineering

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