A Rule Extraction Technique Applied to Ensembles of Neural Networks, Random Forests, and Gradient-Boosted Trees
In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation of new methods of interpretation. A natural way to explain the classifications of t...
Main Author: | Guido Bologna |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-11-01
|
Series: | Algorithms |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4893/14/12/339 |
Similar Items
-
Transferring CNN Features Maps to Ensembles of Explainable Neural Networks
by: Guido Bologna
Published: (2023-02-01) -
Ensemble Tree Machine Learning Models for Improvement of Eurocode 2 Creep Model Prediction
by: Daou Hikmat, et al.
Published: (2022-06-01) -
THE ENSEMBLE METHOD DEVELOPMENT OF CLASSIFICATION OF THE COMPUTER SYSTEM STATE BASED ON DECISIONS TREES
by: Svitlana Gavrylenko, et al.
Published: (2020-10-01) -
In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches
by: Abdulrahman Mohamad Radwan Bulbul, et al.
Published: (2022-11-01) -
Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia
by: Muhammad Muhitur Rahman, et al.
Published: (2023-03-01)