Lelief function implements the algorithm. (Classification weight each feature samples is calculated weight of a selected set of features having the greatest weight. Characteristic is the gene sample). Of the weight of a larger set of pairwise genetic redundancy analysis. The significant correlation between weak and strong correlation in the right gene retention, that is characteristic gene. Number of known gene expression data, samples of different classes, wherein the classification set threshold own weight. Of raw data standardized methods: (x- Gene mean)/variance gene. The between-class distance class of the sample using the Euclidean distance.

Relief特征选择\all_tgf_LIVEMD72_LQAF.mat
..............\choose.m
..............\data_result\feature_gene59.mat
..............\...........\feature_order59.mat
..............\...........\gastric1519.fig
..............\...........\gastric_cancer1519.xls
..............\...........\important_data112.mat
..............\...........\important_order112.mat
..............\...........\数据说明.txt
..............\distant.m
..............\dot_weight.m
..............\gastric_cancer1519.xls
..............\hight.m
..............\ori_imgFeatures72.mat
..............\pillar.m
..............\redundance.m
..............\Relief.m
..............\sharp_jump.m
..............\standardization.m
..............\test.m
..............\weighted.m
..............\data_result
Relief特征选择

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