Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Last update: Dec 23, 2022

Overview

AutoML in Healthcare Review

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.

The curated list of automated feature engineering tools for 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

Automated machine learning pipeline optimizers

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

Neural Architecture Search algorithms, based on performance on the CIFAR-10 dataset

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

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Related tags

Overview

AutoML in Healthcare Review

The curated list of automated feature engineering tools for Automated Machine Learning

Automated machine learning pipeline optimizers

Neural Architecture Search algorithms, based on performance on the CIFAR-10 dataset

Owner

Machine learning that just works, for effortless production applications

Python based GBDT implementation

pandas, scikit-learn, xgboost and seaborn integration

Open source time series library for Python

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)

Decision tree is the most powerful and popular tool for classification and prediction

Classification based on Fuzzy Logic(C-Means).

A Lucid Framework for Transparent and Interpretable Machine Learning Models.

MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions.

Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

Tools for diffing and merging of Jupyter notebooks.

PROTEIN EXPRESSION ANALYSIS FOR DOWN SYNDROME

stability-selection - A scikit-learn compatible implementation of stability selection

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

DaCeML - Machine learning powered by data-centric parallel programming.

This is the code repository for LRM Stochastic watershed model.

This is my implementation on the K-nearest neighbors algorithm from scratch using Python

Simplify stop motion animation with machine learning.

Python/Sage Tool for deriving Scattering Matrices for WDF R-Adaptors

Apache (Py)Spark type annotations (stub files).