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Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors.

Abstract：

OBJECTIVE:We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor classifiers. METHODS AND MATERIAL:Gene expression based cancer classification is used as the task in this study. Six gene expression datasets containing different types of cancer constitute test data. RESULTS:Through extensive simulation coupled with the copula method for analysis of association in bivariate data, we show that dataset complexity and bolstered resubstitution error are associated in terms of dependence. As a result, we propose a new scheme for generating ensembles of classifiers that selects subsets of features of low complexity for ensemble members, which constitutes the accurate members according to the found dependence relation. CONCLUSION:Experiments with six gene expression datasets demonstrate that our ensemble generating scheme based on the dependence of dataset complexity and classification error is superior to a single best classifier in the ensemble and to the traditional ensemble construction scheme that is ignorant of dataset complexity.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Okun O,Priisalu H

doi

10.1016/j.artmed.2008.08.004

subject

Has Abstract

pub_date

2009-02-01 00:00:00

pages

151-62

issue

2-3

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(08)00111-5

journal_volume

45

pub_type

杂志文章

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