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["Table 1) 42 ensemble machine learning methods associated with 18 various cancers"]
["Methods number","42 ensemble methods name","Number of studies that used these 45 methods"]
[1,"BN classifier (1,2)",2]
[2,"Multiple RFE selection method (1,2)",2]
[3,"Feature subsets method",1]
[4,"Multimodal data fusion ",1]
[5,"Meta-classifier ensemble learning based on GP technique",1]
[6,"Ensembles of BioHEL rule sets",1]
[7,"Kernel-based data fusion for gene prioritization",1]
[8,"stSVM (smoothed t-statistic SVM)",1]
[9,"FSCOX-SVM ",1]
[10,"enSVM",1]
[11,"SVM classifiers fusion (three SVM)",1]
[12,"CDSS ",1]
[13,"Bagging subgroup identification trees",1]
[14,"NED method ",1]
[15,"Three NNs fusion",1]
[16,"BNCE",1]
[17,"Meta-learning",1]
[18,"KNNs-SVMs-DT-LDA",1]
[19,"MRS",1]
[20,"Bagging (Bootstrap Aggregation)",1]
[21,"Artificial intelligence-based hybrid ensemble technique",1]
[22,"Boosting-TWSVM",1]
[23,"BBTWSVM",1]
[24,"Ensemble multi-class learning",1]
[25,"RSS-SCS",1]
[26,"REIS-based ensemble method",1]
[27,"MV-ACE",1]
[28,"DECORATE",1]
[29,"HyDRA",1]
[30,"ADASVM",1]
[31,"NB combiner",1]
[32,"Collective approach",1]
[33,"RVM",1]
[34,"Rankboost_W",1]
[35,"PSO–ANN ensemble",1]
[36,"MF-GE system",1]
[37,"Evolutionary Ensemble model",1]
[38,"CAD system",1]
[39,"Optimized NB model",1]
[40,"Stacking-IB3, NBS, RF, SVM",1]
[41,"GenEnsemble (NBS-IB3-SVM-C4.5 DT)",1]
[42,"BNs-based model integration (1,2)",2]