Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Selected highlights from the 2020 AutoML Review [https://doi.org/10.1016/j.artmed.2020.101822] that reviewed over 2,160 works related to the field of automated machine learning.
Full details in https://www.sciencedirect.com/science/article/pii/S0933365719310437?via%3Dihub#tbl0005
Method | Work | Feature Engineering Technique | Used by how many works |
---|---|---|---|
Deep Feature Synthesis | LINK | Expand-Reduce | 151 |
Explore Kit | LINK | Expand-Reduce | 53 |
One Button Machine | LINK | Expand-Reduce | 32 |
AutoLearn | LINK | Expand-Reduce | 16 |
GP Feature Construction | LINK | Genetic Programming | 68 |
Cognito | LINK | Hierarchical Greedy Search | 38 |
RLFE | LINK | Reinforcement Learning | 21 |
LFE | LINK | Meta-Learning | 34 |
Full details in https://www.sciencedirect.com/science/article/pii/S0933365719310437?via%3Dihub#tbl0010
Method | Work | Optimization Algorithm | Data Pre-Processing | Feature Engineering | Model Selection | Hyperparameter Optimization | Ensemble Learning | Meta-Learning | Used by how many works |
---|---|---|---|---|---|---|---|---|---|
Auto-Weka | LINK | Bayesian Optimization (SMAC) | ✔️ | ✔️ | ✔️ | 703 | |||
Auto-Sklearn | LINK | Joint Bayesian Optimization and Bandit Search (BOHB) | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | 542 | |
TPOT | LINK | Evolutionary Algorithm | ✔️ | ✔️ | ✔️ | ✔️ | 84 | ||
TuPAQ | LINK | Bandit Search | ✔️ | ✔️ | 94 | ||||
ATM | LINK | Joint Bayesian Optimization and Bandit Search | ✔️ | ✔️ | ✔️ | 29 | |||
Automatic Frankensteining | LINK | Bayesian Optimization | ✔️ | ✔️ | ✔️ | 12 | |||
ML-Plan | LINK | Hierarchical Task Networks (HTN) | ✔️ | ✔️ | ✔️ | 24 | |||
Autostacker | LINK | Evolutionary Algorithm | ✔️ | ✔️ | ✔️ | 18 | |||
AlphaD3M | LINK | Reinforcement Learning/Monte Carlo Tree Search | ✔️ | ✔️ | ✔️ | 8 | |||
Collaborative Filtering | LINK | Probabilistic Matrix Factorization | ✔️ | ✔️ | ✔️ | ✔️ | 29 |
Full details in https://www.sciencedirect.com/science/article/pii/S0933365719310437?via%3Dihub#tbl0015
NAS Algorithm | Work | Search Space | Search Strategy | Performance Estimation Strategy | Number of Parameters | Search Time (GPU-days) | Test Error (%) |
---|---|---|---|---|---|---|---|
Large-scale Evolution | LINK | Feed-Forward Networks | Evolutionary Algorithm | Naive Training and Validation | 5.4M | 2600 | 5.4 |
EAS | LINK | Feed-Forward Networks | Reinforcement Learning and Network Morphism | Short Training and Validation | 23.4M | 10 | 4.23 |
Hierarchical Evolution | LINK | Cell Motifs | Evolutionary Algorithm | Training and Validation on proposed CNN Cell | 15.7M | 300 | 3.75 |
NAS v3 | LINK | Multi-branched Networks | Reinforcement Learning | Naive Training and Validation | 37.4M | 22400 | 3.65 |
PNAS | LINK | Cell Motifs | Sequential Model-Based Optimization (SMBO) | Performance Prediction | 3.2M | 225 | 3.41 |
ENAS | LINK | Cell Motifs | Reinforcement Learning | One Shot | 4.6M | 0.45 | 2.89 |
ResNet + Regularization | LINK | HUMAN BASELINE | HUMAN BASELINE | HUMAN BASELINE | 26.2M | - | 2.86 |
DARTS | LINK | Cell Motifs | Gradient-Based Optimization | Training and Validation on proposed CNN Cell | 3.4M | 4 | 2.83 |
NASNet-A | LINK | Cell Motifs | Reinforcement Learning | Naive Training and Validation | 3.3M | 2000 | 2.65 |
EENA | LINK | Cell Motifs | Evolutionary Algorithm | Performance Prediction | 8.5M | 0.65 | 2.56 |
Path-Level EAS | LINK | Cell Motifs | Reinforcement Learning | Short Training and Validation | 14.3M | 200 | 2.30 |
NAO | LINK | Cell Motifs | Gradient-Based Optimization | Performance Prediction | 128M | 200 | 2.11 |