scholarly journals Predictions from machine learning ensembles: marine bird distribution and density on Canada’s Pacific coast

2017 ◽  
Vol 566 ◽  
pp. 199-216 ◽  
Author(s):  
CH Fox ◽  
FH Huettmann ◽  
GKA Harvey ◽  
KH Morgan ◽  
J Robinson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pacific Coast ◽  
Marine Bird ◽  
Learning Ensembles ◽  
Bird Distribution

scholarly journals Forecasting Corn Yield With Machine Learning Ensembles

2020 ◽  
Vol 11 ◽  
Author(s):  
Mohsen Shahhosseini ◽  
Guiping Hu ◽  
Sotirios V. Archontoulis
Keyword(s):  
Machine Learning ◽  
Corn Yield ◽  
Learning Ensembles

Machine learning ensembles for wind power prediction

2016 ◽  
Vol 89 ◽  
pp. 671-679 ◽  
Author(s):  
Justin Heinermann ◽  
Oliver Kramer
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Learning Ensembles

Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

2017 ◽  
Vol 26 (05) ◽  
pp. 1760020 ◽  
Author(s):  
Tomáš Křen ◽  
Martin Pilát ◽  
Roman Neruda
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Grid Search ◽  
Current Version ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Ensembles ◽  
New Machine ◽  
The Way

Manual creation of machine learning ensembles is a hard and tedious task which requires an expert and a lot of time. In this work we describe a new version of the GP-ML algorithm which uses genetic programming to create machine learning workows (combinations of preprocessing, classification, and ensembles) automatically, using strongly typed genetic programming and asynchronous evolution. The current version improves the way in which the individuals in the genetic programming are created and allows for much larger workows. Additionally, we added new machine learning methods. The algorithm is compared to the grid search of the base methods and to its previous versions on a set of problems from the UCI machine learning repository.

scholarly journals Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-recidivism Policies in Colombia

2016 ◽  
Vol 24 (4) ◽  
pp. 434-456 ◽  
Author(s):  
Cyrus Samii ◽  
Laura Paler ◽  
Sarah Zukerman Daly
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Regression Modeling ◽  
Causal Effects ◽  
Policy Options ◽  
Micro Data ◽  
Intervention Effect ◽  
New Methods ◽  
Conventional Methods ◽  
Learning Ensembles

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well-defined “retrospective intervention effect” based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.

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