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Local feature attribution methods are increasingly used to explain complex machine learning models.
“Explainable AI for Trees: From Local Explanations to Global Understanding”
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“When Explanations Lie: Why Many Modified BP Attributions Fail”
Leon Sixt, Maximilian Granz and Tim Landgraf · 1912
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“Classification and regression trees”
Leo Breiman, Jerome Friedman, Charles Stone and Richard Olshen · 1984
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“Stacked generalization”
David Wolpert · 1992
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“Classification trees with neural network feature extraction”
Heng Guo and Saul Gelfand · 1992
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“Plan and operation of the NHANES I Epidemiologic Followup Study, 1992”
Christine Cox · 1998
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“The MNIST database of handwritten digits”
Yann LeCun · 1998
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“Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles”
Aravind Subramanian et al · 2005
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“Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition”
Shuang-Quan Wang, Jie Yang and Kuo-Chen Chou · 2006
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“EEG signal classification using wavelet feature extraction and neural networks”
Pari Jahankhani, Vassilis Kodogiannis and Kenneth Revett · 2006
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“Model combination for credit risk assessment: A stacked generalization approach”
Michael Doumpos and Constantin Zopounidis · 2007
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“Mechanisms of mitochondrial dysfunction and energy deficiency in Alzheimer’s disease”
Hani Atamna and William Frey · 2007
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“Learning multiple layers of features from tiny images”, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
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“An efficient explanation of individual classifications using game theory”
Erik Strumbelj and Igor Kononenko · 2010
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“Overview and findings from the religious orders study”
David A, Julie A, Zoe Arvanitakis and Robert S · 2012
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“The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups”
Christina Curtis et al · 2012
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“Deep learning of feature representation with multiple instance learning for medical image analysis”
Yan Xu et al · 2014
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“The scoring of America”
Pam Dixon and Robert Gellman · 2014
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“Secret consumer scores and segmentations: Separating haves from have-nots”
Amy Schmitz · 2014
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“Deep feature extraction and classification of hyperspectral images based on convolutional neural networks”
Yushi Chen et al · 2016
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“"Why should I trust you?" Explaining the predictions of any classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
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“The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes”
Bernard Pereira et al · 2016
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“Comparative metabolic and lipidomic profiling of human breast cancer cells with different metastatic potentials”
“Religious orders study and rush memory and aging project”
David Bennett et al · 2018
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“Purine-related metabolites and their converting enzymes are altered in frontal, parietal and temporal cortex at early stages of Alzheimer’s disease pathology”
Patricia Alonso-Andres, Jose Albasanz, Isidro Ferrer and Mairena Martin · 2018
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“Feature relevance quantification in explainable AI: A causality problem”
Dominik Janzing, Lenon Minorics and Patrick Blöbaum · 2019
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“CXPlain: Causal explanations for model interpretation under uncertainty”
Patrick Schwab and Walter Karlen · 2019
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“Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead”
Cynthia Rudin · 2019
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Hye-Youn Kim et al · 2016
Cited alongside, same era.
“Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization”
Samir Bhatt et al · 2017
Cited alongside, same era.
“Text feature extraction based on deep learning: a review”
Hong Liang, Xiao Sun, Yunlei Sun and Yuan Gao · 2017
Cited alongside, same era.
“A unified approach to interpreting model predictions”
Scott Lundberg and Su-In Lee · 2017
Cited alongside, same era.
“Learning important features through propagating activation differences”
Avanti Shrikumar, Peyton Greenside and Anshul Kundaje · 2017
Cited alongside, same era.
“Axiomatic attribution for deep networks”
Mukund Sundararajan, Ankur Taly and Qiqi Yan · 2017
Cited alongside, same era.
“Differential overexpression of SERPINA3 in human prion diseases”
Silvia Vanni et al · 2017
Cited alongside, same era.
“Generalized Integrated Gradients: A practical method for explaining diverse ensembles”
John Merrill et al · 2019
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“A benchmark for interpretability methods in deep neural networks”
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans and Been Kim · 2019
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“Credit Scoring: FICO, VantageScore; Other Models”
Bill Fay · 2020
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“The many Shapley values for model explanation”
Mukund Sundararajan and Amir Najmi · 2020
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“The Explanation Game: Explaining Machine Learning Models Using Shapley Values”
Luke Merrick and Ankur Taly · 2020
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“Problems with Shapley-value-based explanations as feature importance measures”
I Kumar, Suresh Venkatasubramanian, Carlos Scheidegger and Sorelle Friedler · 2020
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“Shapley-based explainability on the data manifold”
Christopher Frye et al · 2020
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“True to the Model or True to the Data?”
Hugh Chen, Joseph Janizek, Scott Lundberg and Su-In Lee · 2020
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“The AD Knowledge Portal: A Repository for Multi-Omic Data on Alzheimer’s Disease and Aging”
Anna Greenwood et al · 2020
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“FICO, Xml challenge” [Online; accessed 01-June-2021], https://community.fico.com/s/explainable-machine-learning-challenge
2021
Closest in time.
“Ubiquitin and breast cancer”
Tomohiko Ohta and Mamoru Fukuda · 2088
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