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Feature importance ranking has become a powerful tool for explainable AI.
Regression shrinkage and selection via the LASSO
R. Tibshirani · 1996
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Regression shrinkage and selection via the LASSO
R. Tibshirani · 1996
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D. H. Wolpert and W. G. Macready · 1997
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The Element of Statistical Learning
J. Friedman, T. Hastie, and R. Tibshirani · 2001
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Random forests
L. Breiman · 2001
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Random forests
L. Breiman · 2001
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Gene selection for cancer classification using support vector machines
I. Guyon, J. Weston, and S. Barnhill · 2002
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Gene selection for cancer classification using support vector machines
I. Guyon, J. Weston, and S. Barnhill · 2002
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An introduction to variable and feature selection
I. Guyon and A. Elisseeff · 2003
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Use of zero-norm with linear models and kernel methods
J. Weston, A. Elisseeff, and B. Scholkopf · 2003
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An introduction to variable and feature selection
I. Guyon and A. Elisseeff · 2003
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Regularization and variable selection via the elastic net
H. Zou and T. Hastie · 2005
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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
H. Peng, F. Long, and C. Ding · 2005
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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
H. Peng, F. Long, and C. Ding · 2005
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Minimum redundancy feature selection from microarray gene expression data
H. Peng C. Ding · 2005
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Model selection and estimation in regression with grouped variables
M. Yuan and Y. Lin · 2006
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Supervised feature selection via dependence estimation
L. Song, A. Smola, A. Gretton, K. M. Borgwardt, and J. Bedo · 2007
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Supervised feature selection via dependence estimation
L. Song, A. Smola, A. Gretton, K. M. Borgwardt, and J. Bedo · 2007
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Selecting differentially expressed genes using minimum probability of classification error
Pritha Mahata and Kaushik Mahata · 2007
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Understanding the role of noise in stochastic local search: Analysis and experiments
O. J. Mengshoel · 2008
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Ensemble gene selection for cancer classification
Huawen Liu, Lei Liu, and Huijie Zhang · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Feature selection via dependence maximization
L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt · 2012
Deep feature selection: Theory and application to identify enhancers and promoters
Y. Li, C. Y. Chen, and W. Wasserman · 2016
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Interpretation of prediction models using the input gradient
H. Yotam · 2016
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Why should I trust you?: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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W. Samek, T. Wiegand, and K. R. Müller · 2017
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Kernel feature selection via conditional covariance minimization
J. Chen, M. Stern, M. Wainwright, and M. Jordan · 2017
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Kernel feature selection via conditional covariance minimization
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Cited alongside, same era.
Feature selection via dependence maximization
L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt · 2012
Cited alongside, same era.
An atlas of active enhancers across human cell types and tissues
R. Anderson, C. Gebard, I. Miguel-Escalada, et al · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinv · 2014
Cited alongside, same era.
An atlas of active enhancers across human cell types and tissues
R. Anderson, C. Gebard, I. Miguel-Escalada, et al · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Cited alongside, same era.
J. Chen, M. Stern, M. Wainwright, and M. Jordan · 2017
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Multiclass cancer classification using a feature subset-based ensemble from microrna expression profiles
Yongjun Piao, Minghao Piao, and Keun Ho Ryu · 2017
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Feature selection: A data perspective
J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu · 2018
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Classification and diagnostic prediction of breast cancers via different classifiers
Ahmet Saygili · 2018
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A benchmark for interpretability methods in deep neural networks
S. Hooker, D. Erhan, P. J. Kindermans, and B. Kim · 2019
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Global aggregations of local explanations for black box models
I. van der Linden, H. Haned, and E. Kanoulas · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. V. Le · 2019
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Global aggregations of local explanations for black box models
I. van der Linden, H. Haned, and E. Kanoulas · 2019
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Feature importance estimation with self-attention networks
B. Skrlj, S. Dzeroski, N. Lavrac, and M. Petkovic · 2020
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Feature importance estimation with self-attention networks
B. Skrlj, S. Dzeroski, N. Lavrac, and M. Petkovic · 2020
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Unsupervised feature selection algorithm for multiclass cancer classification of gene expression rna-seq data
Pilar García-Díaz, Isabel Sánchez-Berriel, Juan A. Martínez-Rojas, and Ana M. Diez-Pascual · 2020
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