Table 1 22 ensemble learning methods concerned with the detection of breast cancer
From: EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer
| Â | Method name | Publication year |
|---|---|---|
1 | 2006 and 2019 | |
2 | RSS-SCS method [16] | 2016 |
3 | Collective approach (correlation, color palette, color proportion, and SVM) [17] | 2016 |
4 | Kernel-based Data Fusion Method for Gene Prioritization [18] | 2015 |
5 | DECORATE methoda [19] | 2015 |
6 | HyDRA methoda [20] | 2015 |
7 | GenEnsemble methoda (NBS-IB3-SVM-C4.5 DT) [21] | 2014 |
8 | NB (Naïve Bayes) combiner method [22] | 2014 |
9 | Evolutionary Ensemble Model [23] | 2014 |
10 | smoothed t-statistic SVM (stSVM) [24] | 2013 |
11 | SVM Classifiers Fusion (three SVM) [25] | 2013 |
12 | COMBINER (Core Module Biomarker Identification)a [26] | 2012 |
13 | Ensembles of BioHEL Rule Set [27] | 2012 |
14 | Stacking IB3-NBS-RF-SVM method [28] | 2012 |
15 | REIS-based ensemble method [29] | 2011 |
16 | MRS method [30] | 2010 |
17 | Boosting-TWSVM method [31] | 2009 |
18 | Bagging and boosting-based TWSVM [32] | 2009 |
19 | Feature Subsets Method [33] | 2008 |
20 | BNCE method [34] | 2007 |
21 | Bayesian Network Classifier [35] | 2006 |
22 | enSVM (200 SVM) [36] | 2006 |