Open AccessMicroarray‐based cancer diagnosis: repeated cross‐validation‐based ensemble feature selection
Author(s) -
Güney H.,
Öztoprak H.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.4550
Subject(s) - feature selection , resampling , robustness (evolution) , cross validation , support vector machine , computer science , artificial intelligence , stability (learning theory) , pattern recognition (psychology) , receiver operating characteristic , feature (linguistics) , ensemble learning , data mining , machine learning , biology , linguistics , philosophy , biochemistry , gene
The influence of data resampling on ensemble methods, and repeated cross‐validation (RCV)‐based ensemble feature selection (FS) is proposed. To evaluate the proposed method, support vector machine and its extension and recursive feature elimination were used as the underlying classification and FS techniques, respectively. Experimental evaluation was performed using four microarray datasets. The results show that especially for extremely small signature sizes, increasing ensemble size increases both classification performance and the robustness of gene selection (stability) for both RCV and bootstrap (BS). However, for ensembles of the same size, RCV outperforms BS in terms of performance and especially stability. When compared to the top results obtained by two other studies in which BS is utilised, RCV performs similar or better in terms of area under the receiver operator curve and better in terms of stability.