Explainable AI for Trees: From Local Explanations to Global Understanding

11 May 2019

Papers citing "Explainable AI for Trees: From Local Explanations to Global Understanding"

41 / 41 papers shown

Title
RanDeS: Randomized Delta Superposition for Multi-Model Compression Hangyu Zhou Aaron Gokaslan Volodymyr Kuleshov Bharath Hariharan MoMe 86 0 0 16 May 2025
Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management Priyam Ganguly Ramakrishna Garine Isha Mukherjee 48 0 0 24 Feb 2025
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach Oladipo A. Madamidola Felix Ngobigha Adnane Ez-zizi AAML 95 6 0 07 Jul 2024
Regression Trees Know Calculus Nathan Wycoff 85 0 0 22 May 2024
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations Dan Ley Saumitra Mishra Daniele Magazzeni LRM 95 19 0 26 May 2023
Explaining black box text modules in natural language with language models Chandan Singh Aliyah R. Hsu Richard Antonello Shailee Jain Alexander G. Huth Bin Yu Jianfeng Gao MILM 82 58 0 17 May 2023
Relational Local Explanations V. Borisov Gjergji Kasneci FAtt 70 0 0 23 Dec 2022
Feature Selection with Distance Correlation Ranit Das Gregor Kasieczka David Shih 61 14 0 30 Nov 2022
MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning Vajira Thambawita A. Storaas Steven A. Hicks Pål Halvorsen Michael A. Riegler 89 1 0 30 Nov 2022
An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases Futian Weng Yuanting Ma J. Sun Shijun Shan Qiyuan Li Jianping Zhu Yang Wang Yan Xu 108 0 0 20 Nov 2022
A $k$ -additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning G. D. Pelegrina L. Duarte M. Grabisch FAtt TDI 76 31 0 03 Nov 2022
Supply Chain Characteristics as Predictors of Cyber Risk: A Machine-Learning Assessment Kevin Hu R. Levi Raphael Yahalom El Ghali Zerhouni 19 1 0 27 Oct 2022
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces Iñigo López-Riobóo Botana Carlos Eiras-Franco Julio César Hernández Castro Amparo Alonso-Betanzos 122 0 0 09 Sep 2022
PDD-SHAP: Fast Approximations for Shapley Values using Functional Decomposition Arne Gevaert Yvan Saeys FAtt TDI 45 2 0 26 Aug 2022
Predicting tacrolimus exposure in kidney transplanted patients using machine learning A. Storaas A. Aasberg Pål Halvorsen Michael A. Riegler Inga Strümke 30 1 0 09 May 2022
Explainable Machine Learning for Predicting Homicide Clearance in the United States G. Campedelli 40 17 0 09 Mar 2022
Introducing explainable supervised machine learning into interactive feedback loops for statistical production system Carlos Mougan G. Kanellos Johannes Micheler Jose Martinez Thomas Gottron 58 1 0 07 Feb 2022
Fairness Implications of Encoding Protected Categorical Attributes Carlos Mougan J. Álvarez Salvatore Ruggieri Steffen Staab FaML 79 16 0 27 Jan 2022
Fighting Money Laundering with Statistics and Machine Learning R. Jensen Alexandros Iosifidis 82 16 0 11 Jan 2022
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning Yongchan Kwon James Zou TDI 99 135 0 26 Oct 2021
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees Jilei Yang FAtt 105 37 0 20 Sep 2021
Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition Jan Ittner Lukasz Bolikowski Konstantin Hemker Ricardo Kennedy FAtt 31 7 0 26 Jul 2021
Best of both worlds: local and global explanations with human-understandable concepts Jessica Schrouff Sebastien Baur Shaobo Hou Diana Mincu Eric Loreaux Ralph Blanes James Wexler Alan Karthikesalingam Been Kim FAtt 106 28 0 16 Jun 2021
Pitfalls of Explainable ML: An Industry Perspective Sahil Verma Aditya Lahiri John P. Dickerson Su-In Lee XAI 53 9 0 14 Jun 2021
Evaluating the Correctness of Explainable AI Algorithms for Classification Orcun Yalcin Xiuyi Fan Siyuan Liu XAI FAtt 46 15 0 20 May 2021
Explaining a Series of Models by Propagating Shapley Values Hugh Chen Scott M. Lundberg Su-In Lee TDI FAtt 98 139 0 30 Apr 2021
What Makes a Scientific Paper be Accepted for Publication? Panagiotis Fytas Georgios Rizos Lucia Specia 38 10 0 14 Apr 2021
Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives Javier Del Ser D. Casillas-Pérez L. Cornejo-Bueno Luis Prieto-Godino J. Sanz-Justo C. Casanova-Mateo S. Salcedo-Sanz AI4CE 70 44 0 26 Mar 2021
Shapley values for feature selection: The good, the bad, and the axioms D. Fryer Inga Strümke Hien Nguyen FAtt TDI 105 206 0 22 Feb 2021
Activity Detection from Encrypted Remote Desktop Protocol Traffic Lukasz Lapczyk D. Skillicorn 21 1 0 06 Aug 2020
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach A. Moawad E. Islam Namdoo Kim R. Vijayagopal A. Rousseau Wei Biao Wu 80 5 0 15 Jun 2020
Generalized SHAP: Generating multiple types of explanations in machine learning Dillon Bowen L. Ungar FAtt 55 43 0 12 Jun 2020
Negation Detection for Clinical Text Mining in Russian Anastasia A. Funkner Ksenia Balabaeva Sergey Kovalchuk 11 8 0 10 Apr 2020
Causality-based Explanation of Classification Outcomes Leopoldo Bertossi Jordan Li Maximilian Schleich Dan Suciu Zografoula Vagena XAI CML FAtt 255 46 0 15 Mar 2020
Fairness by Explicability and Adversarial SHAP Learning James M. Hickey Pietro G. Di Stefano V. Vasileiou FAtt FedML 129 19 0 11 Mar 2020
A Framework for Democratizing AI Shakkeel Ahmed Ravi Mula S. Dhavala 48 9 0 01 Jan 2020
Explainable artificial intelligence model to predict acute critical illness from electronic health records S. Lauritsen Mads Kristensen Mathias Vassard Olsen Morten Skaarup Larsen K. M. Lauritsen Marianne Johansson Jørgensen Jeppe Lange B. Thiesson 65 305 0 03 Dec 2019
Explaining Models by Propagating Shapley Values of Local Components Hugh Chen Scott M. Lundberg Su-In Lee FAtt FedML 74 110 0 27 Nov 2019
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 90 12 0 20 Nov 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting Ana Lucic H. Haned Maarten de Rijke 68 64 0 17 Jul 2019
Improving performance of deep learning models with axiomatic attribution priors and expected gradients G. Erion Joseph D. Janizek Pascal Sturmfels Scott M. Lundberg Su-In Lee OOD BDL FAtt 95 82 0 25 Jun 2019