Interpretable Ensemble

Interpretable ensemble methods aim to combine the predictive power of multiple machine learning models with enhanced transparency and understanding of their decision-making processes. Current research focuses on developing ensemble architectures that incorporate interpretable base learners, such as decision trees and hyper-rectangles, and employing techniques like feature graphs and gradient-based methods to explain model predictions. This work is significant because it addresses the critical need for trustworthy AI in high-stakes domains like healthcare and autonomous driving, where understanding model behavior is as important as prediction accuracy.

Papers

October 24, 2024

Inherently Interpretable Tree Ensemble Learning
Zebin Yang, Agus Sudjianto, Xiaoming Li, Aijun Zhang
Decision Tree Random Forest Model Interpretability Ensemble Algorithm Interpretable Ensemble

September 16, 2024

DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving
Songning Lai, Tianlang Xue, Hongru Xiao, Lijie Hu, Jiemin Wu, Ninghui Feng, Runwei Guan, Haicheng Liao, Zhenning Li, Yutao Yue
Autonomous Driving Concept Based Autonomous Driving Model Interpretable Ensemble

April 27, 2024

Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping
Christel Sirocchi, Martin Urschler, Bastian Pfeifer
Inherent Interpretability Graph Based Common Disease Interpretable Machine Learning Interaction Generation Model Interpretability Feature Graph Load Centrality Interpretable Ensemble

August 1, 2023

Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)
Md Sakib Hossain Shovon, M. F. Mridha, Khan Md Hasib, Sultan Alfarhood, Mejdl Safran, Dunren Che
Breast Cancer Interpretable Ensemble Class Imbalanced Tackle Uncertainty

July 24, 2023

Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example
Xinyu Jiang, Haofan Sun, Kamal Choudhary, Houlong Zhuang, Qiong Nian
Machine Learning Ensemble Learning Single Example Interatomic Potential Material Property Prediction Carbon Figure Interpretable Ensemble

June 24, 2023

IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations
Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth
Semantic Representation Knowledge Representation Model Interpretability Interpretable Ensemble

March 15, 2023

Interpretable Ensembles of Hyper-Rectangles as Base Models
Andrei V. Konstantinov, Lev V. Utkin
Black Box Model Ensemble Model Base Model Interpretable Ensemble Effective Ensemble

August 11, 2022

RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin, Elina Francovic-Fontaine, Alexandre Drouin, Jacques Corbeil
Inherent Interpretability Ensemble Learning Interpretable Model Metabolomics Data Interpretable Ensemble Sparse Classification

May 25, 2022

Deep interpretable ensembles
Lucas Kook, Andrea Götschi, Philipp FM Baumann, Torsten Hothorn, Beate Sick
Deep Ensemble Transformer Ensemble Interpretable Ensemble

May 12, 2022

SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts
Antonio Jesús Banegas-Luna, Horacio Pérez-Sánchez
Machine Learning Inherent Interpretability Medical Text Personalized Medicine Interpretable Ensemble Generalizable Machine Learning

January 14, 2022

IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction
Mengyue Zha, Kani Chen, Tong Zhang
Time Series Prediction Univariate Time Series Forecasting Competitive Learning Interpretable Ensemble Explainable Ensemble Interpretable Basis

Interpretable Ensemble

Papers

Inherently Interpretable Tree Ensemble Learning

DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving

Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping

Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)

Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example

IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations

Interpretable Ensembles of Hyper-Rectangles as Base Models

RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data

Deep interpretable ensembles

SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts

IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction