TY - JOUR N2 - The paper presents the fusion approach of different feature selection methods in pattern recognition problems. The following methods are examined: nearest component analysis, Fisher discriminant criterion, refiefF method, stepwise fit, Kolmogorov-Smirnov criteria, T2-test, Kruskall-Wallis test, feature correlation with class, and SVM recursive feature elimination. The sensitivity to the noisy data as well as the repeatability of the most important features are studied. Based on this study, the best selection methods are chosen and applied in the process of selection of the most important genes and gene sequences in a dataset of gene expression microarray in prostate and ovarian cancers. The results of their fusion are presented and discussed. The small selected set of such genes can be treated as biomarkers of cancer. L1 - http://journals.pan.pl/Content/119432/PDF/08_01955_Bpast.No.69(3)_23.06.21_Druk.pdf L2 - http://journals.pan.pl/Content/119432 PY - 2021 IS - 3 EP - e136748 DO - 10.24425/bpasts.2021.136748 KW - diagnostic features KW - selection methods KW - genes KW - recognition KW - biomarkers A1 - Gil, Fabian A1 - Osowski, Stanislaw VL - 69 DA - 10.03.2021 T1 - Fusion of feature selection methods in gene recognition SP - e136748 UR - http://journals.pan.pl/dlibra/publication/edition/119432 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -