Detail publikace

Stability of Feature Selection Algorithms and its Influence on Prediction Accuracy in Biomedical Datasets

DROTÁR, P. SMÉKAL, Z.

Originální název

Stability of Feature Selection Algorithms and its Influence on Prediction Accuracy in Biomedical Datasets

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Feature selection techniques become significant part of many bioinformatics and biomedical applications. Choosing the important features is essential for biomarker discovery, provide better understanding of the data and potentially improve prediction performance. However, as the number of samples in dataset is small, the feature selection tends to be unstable. In this paper, the stability of five popular feature selection techniques is compared and analyzed when original dataset is perturbed by adding, removing or simply resampling the original observations. Next, the feature selection techniques are used as filter prior to AdaBoost classifier and their influence on classification accuracy and Mathews correlation coefficient is compared.

Klíčová slova

feature selection, stability, Dunne stability index, bioinformatics, Adaboost

Autoři

DROTÁR, P.; SMÉKAL, Z.

Vydáno

27. 10. 2014

Nakladatel

IEEE

Místo

Bangkok

ISBN

9781479940752

Kniha

TENCON 2011 - 2011 IEEE Region 10 Conference

Strany od

1

Strany do

4

Strany počet

4

URL

https://ieeexplore.ieee.org/document/7022309

BibTex

@inproceedings{BUT110176,
  author="Peter {Drotár} and Zdeněk {Smékal}",
  title="Stability of Feature Selection Algorithms and its Influence on Prediction Accuracy in Biomedical Datasets",
  booktitle="TENCON 2011 - 2011 IEEE Region 10 Conference",
  year="2014",
  pages="1--4",
  publisher="IEEE",
  address="Bangkok",
  doi="10.1109/TENCON.2014.7022309",
  isbn="9781479940752",
  url="https://ieeexplore.ieee.org/document/7022309"
}