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Robust feature selection by weighted Fisher criterion for multiclass prediction in gene expression profiling

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6 Author(s)
Jianhua Xuan ; Dept. of Electr. Eng. & Comput. Sci., Catholic Univ. of America, Washington, DC, USA ; Yibin Dong ; Khan, J. ; Hoffman, E.
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This work presents a robust feature selection approach for multiclass prediction with application to microarray studies. First, individually discriminatory genes (IDGs) are identified using a weighted Fisher criterion (wFC). Second, jointly discriminatory genes (JDGs) are selected by a sequential search method, according to their joint class separability. To combat the small size effect on feature selection, leave-one-out procedures are incorporated into both IDG and JDG selection steps to improve the robustness of the approach. By applying this approach to a microarray study of small round blue cell tumors (SRBCTs) of childhood, we have demonstrated that our robust feature selection method can be used to successfully identify a subset of genes with superior classification performance for multiclass prediction.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:2 )

Date of Conference:

23-26 Aug. 2004