暂无分享,去创建一个
[1] Ambuj K. Singh,et al. Outlier Detection from Network Data with Subnetwork Interpretation , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[2] A. Charnes,et al. Extremal Principle Solutions of Games in Characteristic Function Form: Core, Chebychev and Shapley Value Generalizations , 1988 .
[3] L. Shapley. A Value for n-person Games , 1988 .
[4] Shigeki Takeuchi,et al. Consistent and Efficient Nonparametric Different-Feature Selection , 2017, AISTATS.
[5] Markus H. Gross,et al. Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation , 2019, ICML.
[6] Donghwa Shin,et al. Contextual Outlier Interpretation , 2017, IJCAI.
[7] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[8] Mukund Sundararajan,et al. The many Shapley values for model explanation , 2019, ICML.
[9] Ali A. Ghorbani,et al. A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.
[10] Hans-Peter Kriegel,et al. Outlier Detection in Arbitrarily Oriented Subspaces , 2012, 2012 IEEE 12th International Conference on Data Mining.
[11] Erik Strumbelj,et al. Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.
[12] David A. Clifton,et al. A review of novelty detection , 2014, Signal Process..
[13] Manish Marwah,et al. ACE – An Anomaly Contribution Explainer for Cyber-Security Applications , 2019, 2019 IEEE International Conference on Big Data (Big Data).
[14] Ioana Giurgiu,et al. Additive Explanations for Anomalies Detected from Multivariate Temporal Data , 2019, CIKM.
[15] Kjersti Aas,et al. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values , 2019, Artif. Intell..
[16] Bo Zong,et al. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.
[17] Art B. Owen,et al. On Shapley Value for Measuring Importance of Dependent Inputs , 2016, SIAM/ASA J. Uncertain. Quantification.
[18] Naoya Takeishi. Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection , 2019, 2019 International Conference on Data Mining Workshops (ICDMW).
[19] Lior Rokach,et al. Explaining Anomalies Detected by Autoencoders Using SHAP , 2019, ArXiv.
[20] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[21] Yair Zick,et al. Algorithmic Transparency via Quantitative Input Influence , 2017 .
[22] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[23] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[24] Klemens Böhm,et al. HiCS: High Contrast Subspaces for Density-Based Outlier Ranking , 2012, 2012 IEEE 28th International Conference on Data Engineering.
[25] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[26] Thomas G. Dietterich,et al. Sequential Feature Explanations for Anomaly Detection , 2019, ACM Trans. Knowl. Discov. Data.
[27] James Bailey,et al. Scalable Outlying-Inlying Aspects Discovery via Feature Ranking , 2015, PAKDD.
[28] Daniel Gómez,et al. Improving polynomial estimation of the Shapley value by stratified random sampling with optimum allocation , 2017, Comput. Oper. Res..
[29] Martin Kopp,et al. Anomaly explanation with random forests , 2020, Expert Syst. Appl..
[30] S. Lipovetsky. Entropy Criterion In Logistic Regression And Shapley Value Of Predictors , 2006 .