Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

@article{Alonso2012MicroarrayGE,
  title={Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods},
  author={Carlos J. Alonso and Q. Isaac Moro and Mar{\'i}a Arancha Sim{\'o}n Hurtado and Ricardo Varela-Arrabal},
  journal={Expert Syst. Appl.},
  year={2012},
  volume={39},
  pages={7270-7280}
}
Microarray data classification is a task involving high dimensionality and small samples sizes. A common criterion to decide on the number of selected genes is maximizing the accuracy, which risks overfitting and usually selects more genes than actually needed. We propose, relaxing the maximum accuracy criterion, to select the combination of attribute selection and classification algorithm that using less attributes has an accuracy not statistically significantly worst that the best. Also we… CONTINUE READING

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