Table 1 Summary of related work studies
From: RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data
Authors | Year | #Datasets | Feature Reduction | Classifier |
|---|---|---|---|---|
No Feature Reduction | ||||
 Li et al. [26] | 2022 | 4 | - | SMOTE Resampling + L2-SVM |
 Kakati et al [27] | 2022 | 17 | - | Transfer learning + CNN |
 Dai et al. [28] | 2021 | 3 | - | ERGCN |
Single Stage Feature Selection | ||||
 Mohammed et al. [29] | 2021 | 5 | Lasso | Staking Ensemble of CNN |
 Menaga et al. [30] | 2021 | 2 | Wrapper | Fractional-ASO Deep RNN |
 Al Mamun et al. [31] | 2021 | 12 | mrCAE | - |
Multiple Stages Feature Selection | ||||
 Majumder et al. [33] | 2022 | 4 | ANOVA, IG | MLP, 1DCNN, 2DCNN |
 Saberi-Movahed et al. [34] | 2022 | 9 | DR-FS-MFMR = Matrix Factorization + Minimum Redundancy | Unsupervised clustering |
 Bustamam et al. [35] | 2021 | 2 | SVM-RFE + ABC | SVM |
 Samieinasab et al. [47] | 2022 | 1 | Ensemble (Variance Inflation Factor, Pearson’s Correlation, Information Gain) | Ensemble (Boosting, Bagging, Voting) |
Single Stage Feature Extraction | ||||
 Devendran et al. [38] | 2021 | 2 | PPCA | FBBO + CNN |
 Majji et al. [39] | 2021 | 4 | Non-negative matrix factorization | JayaALO-based Deep RNN |
 Singh et al. [48] | 2022 | 2 | PCA | C5.0, AdaBoost, CART, GBM, NB, RF, SVM, AdaBoost |
 Bacha et al. [50] | 2022 | 2 | KPCA | DE-RBF-KELM |
Feature Selection + Feature Extraction | ||||
 Pandit et.al [44] | 2022 | 5 | Binomial Clustering + Multifractional Brownian Motion + Cuckoo search optimization | Wavelet + CNN |
 Uzma et al. [45] | 2022 | 5 | Two stages: 1-Ensemble (PCA, Correlation, SFS) 2-Autoencoder + GA + K-means | SVM, KNN, RF |