Boosting and Microarray Data

Boosting and Microarray Data (2003)

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by Philip M. Long , Vinsensius Berlian Vega

Venue:	MACHINE LEARNING
Citations:	14 - 1 self

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@INPROCEEDINGS{Long03boostingand,
    author = {Philip M. Long and Vinsensius Berlian Vega},
    title = {Boosting and Microarray Data},
    booktitle = {MACHINE LEARNING},
    year = {2003},
    pages = {2003},
    publisher = {}
}

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Abstract

We have found one reason why AdaBoost tends not to perform well on gene expression data, and identified simple modifications that improve its ability to find accurate class prediction rules. These modifications appear especially to be needed when there is a strong association between expression profiles and class designations. Cross-validation analysis of six microarray datasets with different characteristics suggests that, suitably modified, boosting provides competitive classification accuracy in general. Sometimes the goal

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