Algorithms for interpretable machine learning
暂无分享,去创建一个
[1] Jude W. Shavlik,et al. Extracting refined rules from knowledge-based neural networks , 2004, Machine Learning.
[2] Jude W. Shavlik,et al. in Advances in Neural Information Processing , 1996 .
[3] K. Crawford,et al. Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms , 2013 .
[4] Erik Strumbelj,et al. Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.
[5] Susan T. Dumais,et al. Personalized information delivery: an analysis of information filtering methods , 1992, CACM.
[6] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[7] John Riedl,et al. Explaining collaborative filtering recommendations , 2000, CSCW '00.
[8] Constantin F. Aliferis,et al. An evaluation of machine-learning methods for predicting pneumonia mortality , 1997, Artif. Intell. Medicine.
[9] Markus Zanker,et al. Knowledgeable Explanations for Recommender Systems , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.
[10] C. Kuner. The European Commission's Proposed Data Protection Regulation: A Copernican Revolution in European Data Protection Law , 2012 .
[11] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[12] Erik Strumbelj,et al. Towards a Model Independent Method for Explaining Classification for Individual Instances , 2008, DaWaK.
[13] Seth Flaxman,et al. European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" , 2016, AI Mag..
[14] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[15] T. Graepel,et al. Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.
[16] Kwan-Liu Ma,et al. Opening the black box - data driven visualization of neural networks , 2005, VIS 05. IEEE Visualization, 2005..
[17] Andrew D. Selbst,et al. Big Data's Disparate Impact , 2016 .
[18] Igor Kononenko,et al. Inductive and Bayesian learning in medical diagnosis , 1993, Appl. Artif. Intell..
[19] Jacek M. Zurada,et al. Comparisons of the Performance of Computational Intelligence Methods for Loan Granting Decisions , 2011, 2011 44th Hawaii International Conference on System Sciences.
[20] Kotagiri Ramamohanarao,et al. DeEPs: A New Instance-Based Lazy Discovery and Classification System , 2004, Machine Learning.
[21] Andrew McCallum,et al. A comparison of event models for naive bayes text classification , 1998, AAAI 1998.
[22] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[23] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[24] Ashutosh Kumar Singh,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .
[25] W. Paul Vogt,et al. The SAGE Dictionary of Statistics & Methodology: A Nontechnical Guide for the Social Sciences , 2015 .
[26] Peter Clark,et al. The CN2 Induction Algorithm , 1989, Machine Learning.
[27] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[28] Johannes Gehrke,et al. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.
[29] Rich Caruana,et al. Predicting good probabilities with supervised learning , 2005, ICML.
[30] S. Rüping. Interpreting Classifiers by Multiple Views , 2005 .
[31] Vipin Kumar,et al. Introduction to Data Mining, (First Edition) , 2005 .
[32] Johannes Gehrke,et al. Intelligible models for classification and regression , 2012, KDD.
[33] Miha Vuk,et al. ROC curve, lift chart and calibration plot , 2006, Advances in Methodology and Statistics.
[34] I. Askira-Gelman,et al. Knowledge discovery: comprehensibility of the results , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.
[35] Bianca Zadrozny,et al. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.
[36] Jerzy W. Grzymala-Busse,et al. Rule Induction , 2005, Data Mining and Knowledge Discovery Handbook.
[37] John David N. Dionisio,et al. Case-based explanation of non-case-based learning methods , 1999, AMIA.
[38] J. Lubsen,et al. A Practical Device for the Application of a Diagnostic or Prognostic Function , 1978, Methods of Information in Medicine.
[39] Ross D. Shachter,et al. Patient-specific explanation in models of chronic disease , 1992, Artif. Intell. Medicine.
[40] D. Hand,et al. Idiot's Bayes—Not So Stupid After All? , 2001 .
[41] Rakesh Agarwal,et al. Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.
[42] Cynthia Rudin,et al. Methods and Models for Interpretable Linear Classification , 2014, ArXiv.
[43] Jian Pei,et al. Mining frequent patterns without candidate generation , 2000, SIGMOD '00.
[44] klausdieterborchardt. Treaty on the Functioning of the European Union – A Commentary: Volume I: Preamble, Articles 1-89 , 2017, Springer Commentaries on International and European Law.
[45] David W. Aha,et al. A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.
[46] Martin Mozina,et al. Nomograms for Visualization of Naive Bayesian Classifier , 2004, PKDD.
[47] Jian Pei,et al. CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[48] Daniel T. Larose,et al. Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .
[49] Marko Robnik-Sikonja,et al. Explaining Classifications For Individual Instances , 2008, IEEE Transactions on Knowledge and Data Engineering.
[50] Izak Benbasat,et al. Behavioral Aspects of Information Processing for the Design of Management Information Systems , 1982, IEEE Transactions on Systems, Man, and Cybernetics.
[51] Li Chen,et al. Trust building with explanation interfaces , 2006, IUI '06.
[52] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[53] François Poulet,et al. SVM and graphical algorithms: a cooperative approach , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[54] Erik Strumbelj,et al. Explaining instance classifications with interactions of subsets of feature values , 2009, Data Knowl. Eng..
[55] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .
[56] K. Pearson. The Grammar of Science , 1892, Nature.
[57] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[58] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[59] Petra Perner,et al. Data Mining - Concepts and Techniques , 2002, Künstliche Intell..
[60] D. Kibler,et al. Instance-based learning algorithms , 2004, Machine Learning.
[61] Vasant Honavar,et al. Gaining insights into support vector machine pattern classifiers using projection-based tour methods , 2001, KDD '01.
[62] Nuria Oliver,et al. The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good , 2016, ArXiv.
[63] R. Mike Cameron-Jones,et al. FOIL: A Midterm Report , 1993, ECML.
[64] Bart Baesens,et al. An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models , 2011, Decis. Support Syst..
[65] Toon Calders,et al. Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.
[66] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[67] Claus Weihs,et al. Combining Mental Fit and Data Fit for Classification Rule Selection , 2001 .
[68] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[69] Udo Seiffert,et al. Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size , 2014, Neurocomputing.
[70] Alex Alves Freitas,et al. Comprehensible classification models: a position paper , 2014, SKDD.
[71] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[72] Wynne Hsu,et al. Integrating Classification and Association Rule Mining , 1998, KDD.
[73] Geert Wets,et al. From Decision Tables to Expert System Shells , 1994, Data Knowl. Eng..
[74] Robert M. Colomb,et al. Representation of Propositional Expert Systems as Partial Functions , 1999, Artif. Intell..
[75] Paul Davidsson,et al. Evaluating learning algorithms and classifiers , 2007, Int. J. Intell. Inf. Database Syst..
[76] Nada Lavrac,et al. Selected techniques for data mining in medicine , 1999, Artif. Intell. Medicine.
[77] P. H. Sönksen,et al. Data mining for indicators of early mortality in a database of clinical records , 2001, Artif. Intell. Medicine.
[78] Jiawei Han,et al. CPAR: Classification based on Predictive Association Rules , 2003, SDM.
[79] Elena Baralis,et al. A Lazy Approach to Associative Classification , 2008, IEEE Transactions on Knowledge and Data Engineering.
[80] Luciano Floridi,et al. Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation , 2017 .
[81] Matjaz Gams,et al. Comprehensibility of Classification Trees–Survey Design , 2019 .
[82] G. A. Miller. THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .
[83] Erik Strumbelj,et al. An Efficient Explanation of Individual Classifications using Game Theory , 2010, J. Mach. Learn. Res..
[84] Usama M. Fayyad,et al. Knowledge Discovery in Databases: An Overview , 1997, ILP.
[85] Hendrik Blockeel,et al. Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble , 2007, ECML.
[86] T. Lombrozo. The structure and function of explanations , 2006, Trends in Cognitive Sciences.
[87] N. Cowan. The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.
[88] Huan Liu,et al. Understanding Neural Networks via Rule Extraction , 1995, IJCAI.
[89] Gustavo E. A. P. A. Batista,et al. How k-nearest neighbor parameters affect its performance , 2009 .
[90] Mark R. Wade,et al. Construction and Assessment of Classification Rules , 1999, Technometrics.
[91] Benoît Frénay,et al. Interpretability of machine learning models and representations: an introduction , 2016, ESANN.
[92] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[93] Ruby B. Lee,et al. Hardware-rooted trust for secure key management and transient trust , 2007, CCS '07.
[94] Niklas Lavesson,et al. User-oriented Assessment of Classification Model Understandability , 2011, SCAI.
[95] Jianyong Wang,et al. HARMONY: Efficiently Mining the Best Rules for Classification , 2005, SDM.
[96] Rok Piltaver,et al. Comprehensibility of Classification Trees – Survey Design Validation , 2014 .
[97] Ivan Bratko,et al. Nomograms for visualizing support vector machines , 2005, KDD '05.
[98] Alexander Mordvintsev,et al. Inceptionism: Going Deeper into Neural Networks , 2015 .
[99] G F Cooper,et al. The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.
[100] Kenney Ng,et al. Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models , 2016, CHI.
[101] D. Pager,et al. The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets. , 2008, Annual review of sociology.
[102] Zachary Chase Lipton. The mythos of model interpretability , 2016, ACM Queue.
[103] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.
[104] Yang Wang,et al. An Overview of Associative Classifiers , 2006, DMIN.
[105] Joachim Diederich,et al. Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..
[106] Ron Kohavi,et al. Visualizing the Simple Bayesian Classi er , 1997 .