A Comparative Evaluation of Anomaly Explanation Algorithms
暂无分享,去创建一个
Vassilis Christophides | Nikolaos Myrtakis | Eric Simon | V. Christophides | Nikolaos Myrtakis | Eric Simon
[1] Arthur Zimek,et al. There and back again: Outlier detection between statistical reasoning and data mining algorithms , 2018, WIREs Data Mining Knowl. Discov..
[2] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[3] Klemens Böhm,et al. Dimension-based subspace search for outlier detection , 2018, International Journal of Data Science and Analytics.
[4] Zhi-Hua Zhou,et al. Isolation Forest , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[5] Leman Akoglu,et al. Explaining anomalies in groups with characterizing subspace rules , 2017, Data Mining and Knowledge Discovery.
[6] Marko Robnik-Sikonja,et al. Explaining Classifications For Individual Instances , 2008, IEEE Transactions on Knowledge and Data Engineering.
[7] Abdul Nurunnabi,et al. Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.
[8] Arthur Zimek,et al. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study , 2016, Data Mining and Knowledge Discovery.
[9] Yue Zhao,et al. PyOD: A Python Toolbox for Scalable Outlier Detection , 2019, J. Mach. Learn. Res..
[10] Erik Strumbelj,et al. Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.
[11] Samuel Madden,et al. MacroBase: Prioritizing Attention in Fast Data , 2016, SIGMOD Conference.
[12] Maurizio Filippone,et al. A comparative evaluation of outlier detection algorithms: Experiments and analyses , 2018, Pattern Recognit..
[13] Hui Xiong,et al. Enhancing data analysis with noise removal , 2006, IEEE Transactions on Knowledge and Data Engineering.
[14] Klemens Böhm,et al. Flexible and adaptive subspace search for outlier analysis , 2013, CIKM.
[15] Haopeng Zhang,et al. EXstream: Explaining Anomalies in Event Stream Monitoring , 2017, EDBT.
[16] Percy Liang,et al. Understanding Black-box Predictions via Influence Functions , 2017, ICML.
[17] James Bailey,et al. Discovering outlying aspects in large datasets , 2016, Data Mining and Knowledge Discovery.
[18] Subutai Ahmad,et al. Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).
[19] Tomás Pevný,et al. Loda: Lightweight on-line detector of anomalies , 2016, Machine Learning.
[20] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[21] Erik Strumbelj,et al. An Efficient Explanation of Individual Classifications using Game Theory , 2010, J. Mach. Learn. Res..
[22] Herman Aguinis,et al. Best-Practice Recommendations for Defining, Identifying, and Handling Outliers , 2013 .
[23] Danna Zhou,et al. d. , 1840, Microbial pathogenesis.
[24] Christos Faloutsos,et al. Beyond Outlier Detection: LookOut for Pictorial Explanation , 2018, ECML/PKDD.
[25] Klemens Böhm,et al. HiCS: High Contrast Subspaces for Density-Based Outlier Ranking , 2012, 2012 IEEE 28th International Conference on Data Engineering.
[26] Kai Ming Ting,et al. Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors , 2016, Machine Learning.
[27] TahaAyman,et al. Anomaly Detection Methods for Categorical Data , 2019 .
[28] Mu Zhu,et al. A Relationship between the Average Precision and the Area Under the ROC Curve , 2015, ICTIR.
[29] Arthur Zimek,et al. ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg" , 2019, ArXiv.
[30] PolyzotisNeoklis,et al. Data Lifecycle Challenges in Production Machine Learning , 2018 .
[31] Laurence A. Wolsey,et al. Best Algorithms for Approximating the Maximum of a Submodular Set Function , 1978, Math. Oper. Res..
[32] Cyrus Shahabi,et al. Distance-based Outlier Detection in Data Streams , 2016, Proc. VLDB Endow..
[33] Vassilis Christophides,et al. A greedy feature selection algorithm for Big Data of high dimensionality , 2018, Machine Learning.
[34] Hans-Peter Kriegel,et al. Angle-based outlier detection in high-dimensional data , 2008, KDD.
[35] Andrea Vedaldi,et al. Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[36] Wojciech Samek,et al. Methods for interpreting and understanding deep neural networks , 2017, Digit. Signal Process..
[37] Samuel Madden,et al. Scorpion: Explaining Away Outliers in Aggregate Queries , 2013, Proc. VLDB Endow..
[38] Charu C. Aggarwal,et al. Subspace Outlier Detection in Linear Time with Randomized Hashing , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[39] Navindra Yadav,et al. ExplainIt! -- A Declarative Root-cause Analysis Engine for Time Series Data , 2019, SIGMOD Conference.
[40] Divesh Srivastava,et al. Empirical glitch explanations , 2014, KDD.
[41] Alessandro Rinaldo,et al. Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection , 2019, NeurIPS.
[42] Dan Suciu,et al. Explaining Query Answers with Explanation-Ready Databases , 2015, Proc. VLDB Endow..
[43] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[44] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[45] B. L. Welch. THE SIGNIFICANCE OF THE DIFFERENCE BETWEEN TWO MEANS WHEN THE POPULATION VARIANCES ARE UNEQUAL , 1938 .
[46] Charu C. Aggarwal,et al. An Introduction to Outlier Analysis , 2013 .
[47] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[48] Seiichi Uchida,et al. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data , 2016, PloS one.
[49] Motoaki Kawanabe,et al. How to Explain Individual Classification Decisions , 2009, J. Mach. Learn. Res..
[50] Hans-Peter Kriegel,et al. Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data , 2009, PAKDD.