Explainable decision forest: Transforming a decision forest into an interpretable tree
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[1] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[2] Lior Rokach,et al. Collective-agreement-based pruning of ensembles , 2009, Comput. Stat. Data Anal..
[3] Mariana Belgiu,et al. Random forest in remote sensing: A review of applications and future directions , 2016 .
[4] Andrew P. Bradley,et al. The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..
[5] Yang Yu,et al. Pareto Ensemble Pruning , 2015, AAAI.
[6] J. Ross Quinlan,et al. Generating Production Rules from Decision Trees , 1987, IJCAI.
[7] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[8] R. Tibshirani,et al. Prototype selection for interpretable classification , 2011, 1202.5933.
[9] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[10] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[11] William Nick Street,et al. Ensemble Pruning Via Semi-definite Programming , 2006, J. Mach. Learn. Res..
[12] Wei Tang,et al. Selective Ensemble of Decision Trees , 2003, RSFDGrC.
[13] Qinghua Hu,et al. EROS: Ensemble rough subspaces , 2007, Pattern Recognit..
[14] Kagan Tumer,et al. Classifier ensembles: Select real-world applications , 2008, Inf. Fusion.
[15] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[16] Rich Caruana,et al. Ensemble selection from libraries of models , 2004, ICML.
[17] Marko Bohanec,et al. Explaining machine learning models in sales predictions , 2017, Expert Syst. Appl..
[18] Hussein Almuallim,et al. Turning majority voting classifiers into a single decision tree , 1998, Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294).
[19] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[20] Bart Baesens,et al. An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models , 2011, Decis. Support Syst..
[21] Pedro M. Domingos. Knowledge Discovery Via Multiple Models , 1998, Intell. Data Anal..
[22] Anton van den Hengel,et al. Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Cynthia Rudin,et al. Falling Rule Lists , 2014, AISTATS.
[24] Peter Clark,et al. The CN2 Induction Algorithm , 1989, Machine Learning.
[25] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[26] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[27] Filip De Turck,et al. GENESIM: genetic extraction of a single, interpretable model , 2016, NIPS 2016.
[28] Chang-an Wu,et al. Forest Pruning Based on Branch Importance , 2017, Comput. Intell. Neurosci..
[29] Lior Rokach,et al. Ensemble learning: A survey , 2018, WIREs Data Mining Knowl. Discov..
[30] C. Apte,et al. Data mining with decision trees and decision rules , 1997, Future Gener. Comput. Syst..
[31] Grigorios Tsoumakas,et al. Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection , 2008, ECAI.
[32] Lior Rokach,et al. Decision forest: Twenty years of research , 2016, Inf. Fusion.
[33] Ivan Bratko,et al. Machine Learning: Between Accuracy and Interpretability , 1997 .
[34] Bernhard Sendhoff,et al. Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[35] B. Roe,et al. Boosted decision trees as an alternative to artificial neural networks for particle identification , 2004, physics/0408124.
[36] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[37] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[38] Andrea Vedaldi,et al. Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[39] D. Geman,et al. Randomized Inquiries About Shape: An Application to Handwritten Digit Recognition. , 1994 .
[40] Sylvio Barbon Junior,et al. Predicting the ripening of papaya fruit with digital imaging and random forests , 2018, Comput. Electron. Agric..
[41] David J. Hand,et al. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.
[42] Hendrik Blockeel,et al. Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble , 2007, ECML.
[43] Tal Z. Zarsky,et al. Incompatible: The GDPR in the Age of Big Data , 2017 .
[44] Alex Alves Freitas,et al. Improving the interpretability of classification rules discovered by an ant colony algorithm , 2013, GECCO '13.
[45] Philip S. Yu,et al. Pruning and dynamic scheduling of cost-sensitive ensembles , 2002, AAAI/IAAI.
[46] Muttukrishnan Rajarajan,et al. PIndroid: A novel Android malware detection system using ensemble learning , 2017 .
[47] Bill Howe,et al. DataSynthesizer: Privacy-Preserving Synthetic Datasets , 2017, SSDBM.
[48] Sotiris B. Kotsiantis,et al. Decision trees: a recent overview , 2011, Artificial Intelligence Review.
[49] Ankur Teredesai,et al. Interpretable Machine Learning in Healthcare , 2018, BCB.
[50] Amina Adadi,et al. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.
[51] Rich Caruana,et al. Model compression , 2006, KDD '06.
[52] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[53] Alex Alves Freitas,et al. Comprehensible classification models: a position paper , 2014, SKDD.
[54] Martin Mozina,et al. Orange: data mining toolbox in python , 2013, J. Mach. Learn. Res..
[55] Xiangliang Zhang,et al. An up-to-date comparison of state-of-the-art classification algorithms , 2017, Expert Syst. Appl..
[56] Cynthia Rudin,et al. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model , 2015, ArXiv.
[57] Lior Rokach,et al. Data Mining with Decision Trees - Theory and Applications , 2007, Series in Machine Perception and Artificial Intelligence.
[58] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[59] Erik Strumbelj,et al. Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.
[60] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[61] Rutvija Pandya,et al. C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning , 2015 .
[62] Sanjay Ranka,et al. Global Model Interpretation Via Recursive Partitioning , 2018, 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).
[63] Lei Wang,et al. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery , 2018, Trans. Inst. Meas. Control.
[64] Jude W. Shavlik,et al. in Advances in Neural Information Processing , 1996 .