Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach

29 June 2016

Papers citing "Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach"

10 / 10 papers shown

Title
Learning Interpretable Rules for Scalable Data Representation and Classification Zhuo Wang Wei Zhang Ning Liu Jianyong Wang 35 7 0 22 Oct 2023
HealthPrism: A Visual Analytics System for Exploring Children's Physical and Mental Health Profiles with Multimodal Data Zhihan Jiang Handi Chen Rui Zhou Jing Deng Xinchen Zhang Running Zhao Cong Xie Yifang Wang Edith C.H. Ngai 19 4 0 23 Jul 2023
Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations Chen Peng Zhengqi Dai Guangping Xia Yajie Niu Yihui Lei 23 0 0 09 Feb 2023
Simplification of Forest Classifiers and Regressors Atsuyoshi Nakamura Kento Sakurada 25 1 0 14 Dec 2022
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box David Dandolo Chiara Masiero Mattia Carletti Davide Dalle Pezze Gian Antonio Susto FAtt LRM 24 23 0 23 Dec 2021
Conclusive Local Interpretation Rules for Random Forests Ioannis Mollas Nick Bassiliades Grigorios Tsoumakas FaML FAtt 29 17 0 13 Apr 2021
Born-Again Tree Ensembles Thibaut Vidal Toni Pacheco Maximilian Schiffer 64 53 0 24 Mar 2020
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 19 12 0 20 Nov 2019
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework Homayun Afrabandpey Tomi Peltola Juho Piironen Aki Vehtari Samuel Kaski 25 3 0 21 Oct 2019
Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness Jiawang Bai Yiming Li Jiawei Li Yong Jiang Shutao Xia 37 15 0 14 Mar 2019