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"

24 / 24 papers shown

Title
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach Oladipo A. Madamidola Felix Ngobigha Adnane Ez-zizi AAML 16 5 0 07 Jul 2024
Regression Trees Know Calculus Nathan Wycoff 31 0 0 22 May 2024
Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques Diyi Liu Hyeonsup Lim M. Uddin Yuandong Liu Lee D. Han Ho-Ling Hwang Shih-Miao Chin 13 0 0 01 Feb 2024
Confident Feature Ranking Bitya Neuhof Y. Benjamini FAtt 29 3 0 28 Jul 2023
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations Dan Ley Saumitra Mishra Daniele Magazzeni LRM 38 16 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-Xia Yu Jianfeng Gao MILM 34 47 0 17 May 2023
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 P. Halvorsen Michael A. Riegler 16 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 34 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 35 27 0 03 Nov 2022
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space Carlos Mougan Klaus Broelemann Gjergji Kasneci T. Tiropanis Steffen Staab FAtt 27 7 0 22 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 21 0 0 09 Sep 2022
PDD-SHAP: Fast Approximations for Shapley Values using Functional Decomposition Arne Gevaert Yvan Saeys FAtt TDI 17 2 0 26 Aug 2022
Predicting tacrolimus exposure in kidney transplanted patients using machine learning A. Storaas A. Aasberg P. Halvorsen Michael A. Riegler Inga Strümke 11 1 0 09 May 2022
Explainable Machine Learning for Predicting Homicide Clearance in the United States G. Campedelli 23 13 0 09 Mar 2022
Fighting Money Laundering with Statistics and Machine Learning R. Jensen Alexandros Iosifidis 36 13 0 11 Jan 2022
Global explainability in aligned image modalities Justin Engelmann Amos Storkey Miguel O. Bernabeu FAtt 30 4 0 17 Dec 2021
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning Yongchan Kwon James Zou TDI 39 122 0 26 Oct 2021
Pitfalls of Explainable ML: An Industry Perspective Sahil Verma Aditya Lahiri John P. Dickerson Su-In Lee XAI 16 9 0 14 Jun 2021
Evaluating the Correctness of Explainable AI Algorithms for Classification Orcun Yalcin Xiuyi Fan Siyuan Liu XAI FAtt 16 15 0 20 May 2021
Shapley values for feature selection: The good, the bad, and the axioms D. Fryer Inga Strümke Hien Nguyen FAtt TDI 6 190 0 22 Feb 2021
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 15 5 0 15 Jun 2020
LionForests: Local Interpretation of Random Forests Ioannis Mollas Nick Bassiliades I. Vlahavas Grigorios Tsoumakas 16 12 0 20 Nov 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 21 80 0 25 Jun 2019
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network Jared Katzman Uri Shaham Jonathan Bates A. Cloninger Tingting Jiang Y. Kluger BDL CML OOD 115 1,231 0 02 Jun 2016