Copertina

Inherently Interpretable Tree Ensemble Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yang, Zebin, Sudjianto, Agus, Li, Xiaoming, Zhang, Aijun
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification
di: Pangia, Andrew, et al.
Pubblicazione: (2024)

An Information-Theoretic Framework for Credit Risk Modeling: Unifying Industry Practice with Statistical Theory for Fair and Interpretable Scorecards
di: Sudjianto, Agus, et al.
Pubblicazione: (2025)

Geometric Dynamics of Consumer Credit Cycles: A Multivector-based Linear-Attention Framework for Explanatory Economic Analysis
di: Sudjianto, Agus, et al.
Pubblicazione: (2025)

Model Validation Practice in Banking: A Structured Approach for Predictive Models
di: Sudjianto, Agus, et al.
Pubblicazione: (2024)

Decomposing Global AUC into Cluster-Level Contributions for Localized Model Diagnostics
di: Sudjianto, Agus, et al.
Pubblicazione: (2025)

Interpretable Machine Learning Based on Functional ANOVA Framework: Algorithms and Comparisons
di: Linwei Hu, et al.
Pubblicazione: (2025)

Automatic Generation of Behavioral Test Cases For Natural Language Processing Using Clustering and Prompting
di: Li, Ying, et al.
Pubblicazione: (2024)

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
di: Mensah, Samuel Ofosu, et al.
Pubblicazione: (2025)

REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
di: Nguyen, Simon D., et al.
Pubblicazione: (2026)

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems
di: Kolicic, Benjamin, et al.
Pubblicazione: (2024)

Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
di: Shen, Yueyang, et al.
Pubblicazione: (2024)

Inherently Interpretable Time Series Classification via Multiple Instance Learning
di: Early, Joseph, et al.
Pubblicazione: (2023)

Human-Calibrated Automated Testing and Validation of Generative Language Models
di: Sudjianto, Agus, et al.
Pubblicazione: (2024)

Interpretable Credit Default Prediction with Ensemble Learning and SHAP
di: Yang, Shiqi, et al.
Pubblicazione: (2025)

Learning Ensembles of Interpretable Simple Structure
di: Arwade, Gaurav, et al.
Pubblicazione: (2025)

SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks
di: Sun, Xiaotong, et al.
Pubblicazione: (2024)

Interpretable Clustering Ensemble
di: Lv, Hang, et al.
Pubblicazione: (2025)

Breaking the Black Box: Inherently Interpretable Physics-Constrained Machine Learning With Weighted Mixed-Effects for Imbalanced Seismic Data
di: Sreenath, Vemula, et al.
Pubblicazione: (2025)

Interpretable Representation Learning for Additive Rule Ensembles
di: Behzadimanesh, Shahrzad, et al.
Pubblicazione: (2025)

Explainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring
di: Kuschel, Maurice, et al.
Pubblicazione: (2026)

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
di: Liu, Brian, et al.
Pubblicazione: (2025)

Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
di: Arrighi, Leonardo, et al.
Pubblicazione: (2024)

Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
di: Chen, Xiaolin, et al.
Pubblicazione: (2021)

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability
di: Bhalla, Usha, et al.
Pubblicazione: (2023)

ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography
di: Ghamary, Yeganeh, et al.
Pubblicazione: (2025)

B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
di: Arya, Shreyash, et al.
Pubblicazione: (2024)

X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis
di: Bongratz, Fabian, et al.
Pubblicazione: (2025)

On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
di: Wei, Rongzhe, et al.
Pubblicazione: (2023)

Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks
di: Zhang, Guanhua, et al.
Pubblicazione: (2024)

Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
di: Gan, Xiaoyu, et al.
Pubblicazione: (2024)

MLOW: Interpretable Low-Rank Frequency Magnitude Decomposition of Multiple Effects for Time Series Forecasting
di: Yang, Runze, et al.
Pubblicazione: (2026)

Trustworthy Tree-based Machine Learning by $MoS_2$ Flash-based Analog CAM with Inherent Soft Boundaries
di: Wen, Bo, et al.
Pubblicazione: (2025)

Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning
di: Chun, Sanghyuk
Pubblicazione: (2025)

Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning
di: Ju, Keyi, et al.
Pubblicazione: (2023)

GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
di: Zhu, Zhongshu, et al.
Pubblicazione: (2024)

Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs
di: Wang, Longwen, et al.
Pubblicazione: (2024)

Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
di: Dai, Rong, et al.
Pubblicazione: (2024)

Option Pricing Using Ensemble Learning
di: Li, Zeyuan, et al.
Pubblicazione: (2025)

Tree-based Ensemble Learning for Out-of-distribution Detection
di: Shen, Zhaiming, et al.
Pubblicazione: (2024)

ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding
di: Zhong, Shuzhang, et al.
Pubblicazione: (2024)