Efficiency of different measures for defining the applicability domain of classification models
暂无分享,去创建一个
Miriam Mathea | Knut Baumann | Nikolaus Heinrich | Waldemar Klingspohn | Antonius ter Laak | N. Heinrich | K. Baumann | M. Mathea | Waldemar Klingspohn | A. ter Laak
[1] Ziding Feng,et al. Evaluating the Predictiveness of a Continuous Marker , 2007, Biometrics.
[2] Alexander Tropsha,et al. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. , 2010, Chemical research in toxicology.
[3] C. K. Chow,et al. On optimum recognition error and reject tradeoff , 1970, IEEE Trans. Inf. Theory.
[4] Sameer Singh,et al. Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..
[5] Scott Boyer,et al. Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays. , 2016, Chemical research in toxicology.
[6] Simon Fong,et al. An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets , 2013, DaEng.
[7] Igor V. Tetko,et al. Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information , 2011, J. Comput. Aided Mol. Des..
[8] Craig Zwickl,et al. An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment. , 2015, Regulatory toxicology and pharmacology : RTP.
[9] Jeremy L. Jenkins,et al. Clustering and Rule-Based Classifications of Chemical Structures Evaluated in the Biological Activity Space , 2007, J. Chem. Inf. Model..
[10] Thomas G. Dietterich,et al. A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction , 1993, NIPS.
[11] Matthieu Montes,et al. Predictiveness curves in virtual screening , 2015, Journal of Cheminformatics.
[12] Arthur Zimek,et al. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study , 2016, Data Mining and Knowledge Discovery.
[13] Thomas Hofmann,et al. Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms , 2002, J. Comput. Biol..
[14] Klaus-Robert Müller,et al. Benchmark Data Set for in Silico Prediction of Ames Mutagenicity , 2009, J. Chem. Inf. Model..
[15] Alex M. Andrew,et al. Boosting: Foundations and Algorithms , 2012 .
[16] Tom Fawcett,et al. ROC graphs with instance-varying costs , 2006, Pattern Recognit. Lett..
[17] W. Gasarch,et al. The Book Review Column 1 Coverage Untyped Systems Simple Types Recursive Types Higher-order Systems General Impression 3 Organization, and Contents of the Book , 2022 .
[18] Roberto Todeschini,et al. Molecular descriptors for chemoinformatics , 2009 .
[19] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[20] Charu C. Aggarwal,et al. Re-designing distance functions and distance-based applications for high dimensional data , 2001, SGMD.
[21] R. Lippmann. Pattern classification using neural networks , 1989, IEEE Communications Magazine.
[22] Constantin F. Aliferis,et al. A gentle introduction to support vector machines in biomedicine: Volume 1: Theory and methods , 2011 .
[23] Roberto Todeschini,et al. Quantitative Structure − Activity Relationship Models for Ready Biodegradability of Chemicals , 2013 .
[24] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[25] Christian Weimar,et al. Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications , 2014, Biometrical journal. Biometrische Zeitschrift.
[26] J. Copas. The Effectiveness of Risk Scores: the Logit Rank Plot , 1999 .
[27] Alexander Gammerman,et al. Conformal Predictors for Compound Activity Prediction , 2016, COPA.
[28] Heikki Mannila,et al. Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.
[29] Yingye Zheng,et al. Integrating the predictiveness of a marker with its performance as a classifier. , 2007, American journal of epidemiology.
[30] J. D. Malley,et al. Probability Machines , 2011, Methods of Information in Medicine.
[31] Knut Baumann,et al. Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation , 2014, Journal of Cheminformatics.
[32] Thomas G. Dietterich,et al. Systematic construction of anomaly detection benchmarks from real data , 2013, ODD '13.
[33] Robert P. W. Duin,et al. Classifier Conditional Posterior Probabilities , 1998, SSPR/SPR.
[34] Robert P. Sheridan,et al. Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest , 2012, J. Chem. Inf. Model..
[35] Ferran Sanz,et al. Anchor-GRIND: filling the gap between standard 3D QSAR and the GRid-INdependent descriptors. , 2005, Journal of medicinal chemistry.
[36] Scott Boyer,et al. Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination , 2014, J. Chem. Inf. Model..
[37] Martin E. Hellman,et al. The Nearest Neighbor Classification Rule with a Reject Option , 1970, IEEE Trans. Syst. Sci. Cybern..
[38] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[39] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[40] Igor V. Tetko,et al. Development of Dimethyl Sulfoxide Solubility Models Using 163 000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions , 2013, J. Chem. Inf. Model..
[41] Jesús Alcalá-Fdez,et al. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..
[42] Weida Tong,et al. Assessment of Prediction Confidence and Domain Extrapolation of Two Structure–Activity Relationship Models for Predicting Estrogen Receptor Binding Activity , 2004, Environmental health perspectives.
[43] J. Franklin,et al. The elements of statistical learning: data mining, inference and prediction , 2005 .
[44] P. Bartlett,et al. Probabilities for SV Machines , 2000 .
[45] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[46] Martin Schumacher. Probability estimation and machine learning--Editorial. , 2014, Biometrical journal. Biometrische Zeitschrift.
[47] I. Tetko,et al. Applicability domain for in silico models to achieve accuracy of experimental measurements , 2010 .
[48] Klaus-Robert Müller,et al. From outliers to prototypes: Ordering data , 2006, Neurocomputing.
[49] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[50] Scott D. Kahn,et al. Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships , 2005, Alternatives to laboratory animals : ATLA.
[51] Victoria J. Hodge,et al. A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.
[52] Chao Lan,et al. Anomaly Detection , 2018, Encyclopedia of GIS.
[53] J. J. Narraway,et al. Probability machines , 1989, Microprocess. Microprogramming.
[54] Felix Naumann,et al. Data fusion , 2009, CSUR.
[55] Scott Boyer,et al. The application of conformal prediction to the drug discovery process , 2013, Annals of Mathematics and Artificial Intelligence.
[56] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[57] M. Kohler,et al. Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory , 2014, Biometrical journal. Biometrische Zeitschrift.
[58] Christopher J. C. Burges,et al. A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.
[59] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[60] Tudor I. Oprea,et al. hERG classification model based on a combination of support vector machine method and GRIND descriptors. , 2008, Molecular pharmaceutics.
[61] R. Todeschini,et al. Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References , 2009 .
[62] David A. Clifton,et al. A review of novelty detection , 2014, Signal Process..
[63] C. Hansch,et al. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .
[64] Bianca Zadrozny,et al. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.
[65] Robert P. Sheridan,et al. Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR , 2004, J. Chem. Inf. Model..
[66] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[67] Robert P. Sheridan,et al. The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity , 2015, J. Chem. Inf. Model..
[68] Igor V. Tetko,et al. Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection , 2008, J. Chem. Inf. Model..
[69] Blaise Hanczar,et al. Classification with reject option in gene expression data , 2008, Bioinform..
[70] Marc Strickert,et al. Target‐Driven Subspace Mapping Methods and Their Applicability Domain Estimation , 2011, Molecular informatics.
[71] T.Y. Lin,et al. Anomaly detection , 1994, Proceedings New Security Paradigms Workshop.
[72] Gisbert Schneider,et al. Deep Learning in Drug Discovery , 2016, Molecular informatics.
[73] Gavin C. Cawley,et al. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..
[74] David Mease,et al. Boosted Classification Trees and Class Probability/Quantile Estimation , 2007, J. Mach. Learn. Res..
[75] Robert P. Sheridan,et al. Using Random Forest To Model the Domain Applicability of Another Random Forest Model , 2013, J. Chem. Inf. Model..
[76] Bianca Zadrozny,et al. Transforming classifier scores into accurate multiclass probability estimates , 2002, KDD.
[77] Richard Simon,et al. Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.
[78] Igor V. Tetko,et al. Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set , 2010, J. Chem. Inf. Model..
[79] Sameer Singh,et al. Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..
[80] A. Bender,et al. Prediction of PARP Inhibition with Proteochemometric Modelling and Conformal Prediction , 2015, Molecular informatics.
[81] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[82] Richard Simon,et al. Class probability estimation for medical studies , 2014, Biometrical journal. Biometrische Zeitschrift.
[83] K. Baumann,et al. Chemoinformatic Classification Methods and their Applicability Domain , 2016, Molecular informatics.
[84] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.