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An evaluation of using ensembles of classifiers for predictions based on genomic and proteomic data

Ringnér, Markus LU orcid and Johansson, Peter LU (2006)
Abstract
Classification of expression profiles to predict disease characteristics of for example cancer is a common application in high-throughput gene and protein expression research. Cross-validation is often used to optimize design of classifiers, with the aim to construct an optimal single classifier. In this work, we explore if classification performance can be improved by aggregating classifiers into ensembles that use committee votes for classification.

We investigated if combining classifiers into ensembles improved classification performance compared to single classifiers. A couple of commonly used classifiers, nearest centroid classifier and support vector machine, were evaluated using four publicly available data sets. We found... (More)
Classification of expression profiles to predict disease characteristics of for example cancer is a common application in high-throughput gene and protein expression research. Cross-validation is often used to optimize design of classifiers, with the aim to construct an optimal single classifier. In this work, we explore if classification performance can be improved by aggregating classifiers into ensembles that use committee votes for classification.

We investigated if combining classifiers into ensembles improved classification performance compared to single classifiers. A couple of commonly used classifiers, nearest centroid classifier and support vector machine, were evaluated using four publicly available data sets. We found ensemble methods generally performed better
than corresponding single classifiers. (Less)
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9 pages
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English
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additional info
LU TP 06-19
id
91a93a11-028e-4d3b-8687-d6f6c19558e6
date added to LUP
2017-12-03 14:32:39
date last changed
2021-01-14 02:19:04
@misc{91a93a11-028e-4d3b-8687-d6f6c19558e6,
  abstract     = {Classification of expression profiles to predict disease characteristics of for example cancer is a common application in high-throughput gene and protein expression research. Cross-validation is often used to optimize design of classifiers, with the aim to construct an optimal single classifier. In this work, we explore if classification performance can be improved by aggregating classifiers into ensembles that use committee votes for classification.<br/><br/>We investigated if combining classifiers into ensembles improved classification performance compared to single classifiers. A couple of commonly used classifiers, nearest centroid classifier and support vector machine, were evaluated using four publicly available data sets. We found ensemble methods generally performed better<br/>than corresponding single classifiers.},
  author       = {Ringnér, Markus and Johansson, Peter},
  language     = {eng},
  title        = {An evaluation of using ensembles of classifiers for predictions based on genomic and proteomic data},
  url          = {https://lup.lub.lu.se/search/files/35394172/lu_tp_06_19.pdf},
  year         = {2006},
}