Table 6.xls (9.5 kB)

Classification metrics (%) of four optimization algorithms for five cancer datasets.

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posted on 2022-04-26, 17:38 authored by Hu Ai

Classification metrics (%) of four optimization algorithms for five cancer datasets.

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three advanced algorithms random forest models matthews correlation coefficients extremely randomized trees survival analysis showed fold classification models gene expression studies corresponding deleted gene svm ), k gsea &# 8211 required classification performance xlink "> selecting respectively computing differences gene selection method xlink "> moreover eliminate redundant genes breast cancer diagnosis analyzing mcc differences respectively using 10 xlink ">classification performance breast cancer analyzing differences sample classification results showed redundant genes eliminate irrelevant allowed selecting gene list xgboost ),performance metrics gsea ),diagnosing cancer respectively sorted vegfd two parts tslp thus determine strongly related stably assembled smallest set smaller number set position select genes right side remaining genes relevant genes proposed method pkmyt1 particularly important optimal solution novel method nearest neighbor methods tested left side kyoto encyclopedia genomes pathways filtered according extratrees ).easy elimination core enrichment continuously iterating common process backward elimination 75 %.

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