Feature Selection with Random Forests Predicting Metagenome-Based Disease

2021 ◽  
pp. 254-266
Author(s):  
Huong Hoang Luong ◽  
Thanh Huyen Nguyen Thi ◽  
An Duc Le ◽  
Hai Thanh Nguyen
Keyword(s):  
Feature Selection ◽  
Random Forests

Feature selection using different evaluate strategy and random forests

2021 ◽  
Author(s):  
Zhuo Wang ◽  
Huan Li ◽  
Bin Nie ◽  
Jianqiang Du ◽  
Yuwen Du ◽  
...  
Keyword(s):  
Feature Selection ◽  
Random Forests

scholarly journals Random KNN feature selection - a fast and stable alternative to Random Forests

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Shengqiao Li ◽  
E James Harner ◽  
Donald A Adjeroh
Keyword(s):  
Feature Selection ◽  
Random Forests

scholarly journals On the Interpretability of Machine Learning Models and Experimental Feature Selection in Case of Multicollinear Data

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 761
Author(s):  
Franc Drobnič ◽  
Andrej Kos ◽  
Matevž Pustišek
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forests ◽  
Experimental Approach ◽  
Feature Selection Method ◽  
Model Quality ◽  
Human In The Loop ◽  
Model Interpretation ◽  
Small Feature ◽  
Original Feature

In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machine learning, but their operation is of “black box” character. Among the quantifiable approaches to the model interpretation, there are measures of association of predictors and response. In case of the Random Forests, this approach usually consists of calculating the model’s feature importances. Known methods, including the built-in one, are less suitable in settings with strong multicollinearity of features. Therefore, we propose an experimental approach to the feature selection task, a greedy forward feature selection method with least-trees-used criterion. It yields a set of most informative features that can be used in a machine learning (ML) training process with similar prediction quality as the original feature set. We verify the results of the proposed method on two known datasets, one with small feature multicollinearity and another with large feature multicollinearity. The proposed method also allows for a domain expert help with selecting among equally important features, which is known as the human-in-the-loop approach.

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