The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Classification and Regression Trees.
Abstract
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
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Cites
(1979) · Cited by 43,841
(1967) · Cited by 23,719
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Information theory and statistics
(1959) · Journal of the Franklin Institute · doi:10.1016/0016-0032(59)90368-0 · Cited by 7,094
Cluster analysis of multivariate data : efficiency versus interpretability of classifications
Forgy, E. W. (1965) · Biometrics · Cited by 2,093
Cover, T. M. (1965) · IEEE Transactions on Electronic Computers · doi:10.1109/pgec.1965.264137 · Cited by 2,044
Markel, J. D., & Gray, A. H. (1976) · Communication and Cybernetics · doi:10.1007/978-3-642-66286-7 · Cited by 1,402
Constructing optimal binary decision trees is NP-complete
Hyafil, L., & Rivest, R. L. (1976) · Information Processing Letters · doi:10.1016/0020-0190(76)90095-8 · Cited by 959
Hunt, E. B., Marin, J., & Stone, P. J. (1966) · Medical Entomology and Zoology · Cited by 691
Speech coding based upon vector quantization
Buzo, A., Gray, A., Gray, R., & Markel, J. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163445 · Cited by 559
Cited by
Items 101–200 of 374
Patterns and uncertainties of species' range shifts under climate change
Thuiller, W. (2004) · Global Change Biology · doi:10.1111/j.1365-2486.2004.00859.x · Cited by 873
Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex
Markov, N. T., Vezoli, J., Chameau, P., Falchier, A., Quilodran, R., Huissoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., et al. (2014) · Journal of Comparative Neurology · doi:10.1002/cne.23458 · Cited by 759
Risk factors for binge eating onset in adolescent girls: A 2-year prospective investigation.
Stice, E., Presnell, K., & Spangler, D. (2002) · Health Psychology · doi:10.1037/0278-6133.21.2.131 · Cited by 835
A review of feature selection methods on synthetic data
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0487-8 · Cited by 830
Predictive data mining in clinical medicine: Current issues and guidelines
BELLAZZI, R., & ZUPAN, B. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.11.006 · Cited by 823
Machine Learning Methods Without Tears: A Primer for Ecologists
Olden, J. D., Lawler, J. J., & Poff, N. L. (2008) · The Quarterly Review of Biology · doi:10.1086/587826 · Cited by 823
An Adaptive Estimation of Dimension Reduction Space
Xia, Y., Tong, H., Li, W. K., & Zhu, L.-X. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.03411 · Cited by 820
What limits fire? An examination of drivers of burnt area in Southern Africa
ARCHIBALD, S., ROY, D. P., Van WILGEN, B. W., & SCHOLES, R. J. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2008.01754.x · Cited by 773
Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802
Learning and evaluating classifiers under sample selection bias
Zadrozny, B. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015425 · Cited by 800
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766
Consumer credit-risk models via machine-learning algorithms
Khandani, A. E., Kim, A. J., & Lo, A. W. (2010) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2010.06.001 · Cited by 864
Activity Classification Using Realistic Data From Wearable Sensors
Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., & Korhonen, I. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.856863 · Cited by 760
C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993
Salzberg, S. L. (1994) · Machine Learning · doi:10.1007/bf00993309 · Cited by 746
Improved discrimination of AD patients using β-amyloid (1-42) and tau levels in CSF
Hulstaert, F., Blennow, K., Ivanoiu, A., Schoonderwaldt, H. C., Riemenschneider, M., Deyn, P. P. D., Bancher, C., Cras, P., Wiltfang, J., Mehta, P. D., et al. (1999) · Neurology · doi:10.1212/wnl.52.8.1555 · Cited by 724
Ben Taieb, S., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.039 · Cited by 734
Chipman, H. A., George, E. I., & McCulloch, R. E. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10473750 · Cited by 734
Decision trees: An overview and their use in medicine
Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I. (2002) · Journal of Medical Systems · doi:10.1023/a:1016409317640 · Cited by 736
Random forest regression for online capacity estimation of lithium-ion batteries
Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C.-W., van den Bossche, P., Van Mierlo, J., & Omar, N. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.182 · Cited by 727
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700
Interpretable Decision Sets: A Joint Framework for Description and Prediction
Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687
Harnessing context sensing to develop a mobile intervention for depression
Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., & Mohr, D. C. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1838 · Cited by 646
(2001) · Cited by 683
Supervised neural networks for the classification of structures
Sperduti, A., & Starita, A. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.572108 · Cited by 666
Baker, M. E., & King, R. S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2009.00007.x · Cited by 658
Callaham, M. (2002) · JAMA · doi:10.1001/jama.287.21.2847 · Cited by 644
Machine learning and its applications to biology
Tarca, A. L., Carey, V. J., Chen, X.-W., Romero, R., & Drăghici, S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030116 · Cited by 656
Sunderland, T., Linker, G., Mirza, N., Putnam, K. T., Friedman, D. L., Kimmel, L. H., Bergeson, J., Manetti, G. J., Zimmermann, M., Tang, B., et al. (2003) · JAMA · doi:10.1001/jama.289.16.2094 · Cited by 656
A survey on fake news and rumour detection techniques
Bondielli, A., & Marcelloni, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.035 · Cited by 658
BURMAN, P. (1989) · Biometrika · doi:10.1093/biomet/76.3.503 · Cited by 634
Global estimation of burned area using MODIS active fire observations
Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., & Kasibhatla, P. (2006) · Atmospheric Chemistry and Physics · doi:10.5194/acp-6-957-2006 · Cited by 635
Evaluating multiple classifiers for stock price direction prediction
Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.05.013 · Cited by 611
Predicting stock market index using fusion of machine learning techniques
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.031 · Cited by 608
Twenty Years of Mixture of Experts
Yuksel, S. E., Wilson, J. N., & Gader, P. D. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2200299 · Cited by 606
Scornet, E., Biau, G., & Vert, J.-P. (2015) · The Annals of Statistics · doi:10.1214/15-aos1321 · Cited by 612
Woody cover in African savannas: the role of resources, fire and herbivory.
Sankaran, M., Ratnam, J., & Hanan, N. (2008) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2007.00360.x · Cited by 589
Let's put garbage-can regressions and garbage-can probits where they belong
Achen, C. H. (2005) · Conflict Management and Peace Science · doi:10.1080/07388940500339167 · Cited by 576
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts
Zhao, Z., Resnick, P., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741637 · Cited by 583
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
Tu, Z., & Bai, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.186 · Cited by 578
Evaluating the economic impact of water scarcity in a changing world
Dolan, F., Lamontagne, J., Link, R., Hejazi, M., Reed, P., & Edmonds, J. (2021) · Nature Communications · doi:10.1038/s41467-021-22194-0 · Cited by 574
Loyola-Gonzalez, O. (2019) · IEEE Access · doi:10.1109/access.2019.2949286 · Cited by 559
Opening the black box: the promise and limitations of explainable machine learning in cardiology
Petch, J., Di, S., & Nelson, W. (2022) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.09.004 · Cited by 552
Deciphering the distribution of the savanna biome
Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A., & Bond, W. J. (2011) · New Phytologist · doi:10.1111/j.1469-8137.2011.03689.x · Cited by 523
Grip Strength Cutpoints for the Identification of Clinically Relevant Weakness
Alley, D. E., Shardell, M. D., Peters, K. W., McLean, R. R., Dam, T.-T. L., Kenny, A. M., Fragala, M. S., Harris, T. B., Kiel, D. P., Guralnik, J. M., et al. (2014) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glu011 · Cited by 535
Statistical decision-tree models for parsing
Magerman, D. M. (1995) · Proceedings of the 33rd annual meeting on Association for Computational Linguistics - · doi:10.3115/981658.981695 · Cited by 567
Bohus, M., Haaf, B., Simms, T., Limberger, M. F., Schmahl, C., Unckel, C., Lieb, K., & Linehan, M. M. (2004) · Behaviour Research and Therapy · doi:10.1016/s0005-7967(03)00174-8 · Cited by 526
Tree Induction for Probability-Based Ranking
Provost, F., & Domingos, P. (2003) · Machine Learning · doi:10.1023/a:1024099825458 · Cited by 541
Boosted decision trees as an alternative to artificial neural networks for particle identification
Roe, B. P., Yang, H.-J., Zhu, J., Liu, Y., Stancu, I., & McGregor, G. (2005) · Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment · doi:10.1016/j.nima.2004.12.018 · Cited by 534
Classification of electrophysiological and morphological neuron types in the mouse visual cortex
Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0417-0 · Cited by 456
Quinlan, J. R., & Cameron-Jones, R. M. (1993) · Lecture Notes in Computer Science · doi:10.1007/3-540-56602-3_124 · Cited by 528
A general approach for predicting the behavior of the Supreme Court of the United States
Katz, D. M., Bommarito, M. J., & Blackman, J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174698 · Cited by 528
Austin, P. C., & Steyerberg, E. W. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280214558972 · Cited by 516
Blended learning: A dangerous idea?
Moskal, P., Dziuban, C., & Hartman, J. (2013) · The Internet and Higher Education · doi:10.1016/j.iheduc.2012.12.001 · Cited by 494
Structure-based classification predicts drug response in EGFR-mutant NSCLC
Robichaux, J. P., Le, X., Vijayan, R. S. K., Hicks, J. K., Heeke, S., Elamin, Y. Y., Lin, H. Y., Udagawa, H., Skoulidis, F., Tran, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03898-1 · Cited by 490
On the Class Imbalance Problem
Guo, X., Yin, Y., Dong, C., Yang, G., & Zhou, G. (2008) · 2008 Fourth International Conference on Natural Computation · doi:10.1109/icnc.2008.871 · Cited by 495
A new measure of prognostic separation in survival data
Royston, P., & Sauerbrei, W. (2004) · Statistics in Medicine · doi:10.1002/sim.1621 · Cited by 450
Ding, C., Cao, X. (., & Næss, P. (2018) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2018.02.009 · Cited by 446
Galasko, D., Chang, L., Motter, R., Clark, C. M., Kaye, J., Knopman, D., Thomas, R., Kholodenko, D., Schenk, D., Lieberburg, I., et al. (1998) · Archives of Neurology · doi:10.1001/archneur.55.7.937 · Cited by 442
Neumark-Sztainer, D., Story, M., Hannan, P. J., & Croll, J. (2002) · American Journal of Public Health · doi:10.2105/ajph.92.5.844 · Cited by 440
Hampel, H., Buerger, K., Zinkowski, R., Teipel, S. J., Goernitz, A., Andreasen, N., Sjoegren, M., DeBernardis, J., Kerkman, D., Ishiguro, K., et al. (2004) · Archives of General Psychiatry · doi:10.1001/archpsyc.61.1.95 · Cited by 434
Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
Dudek, A., Arodz, T., & Galvez, J. (2006) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620706776055539 · Cited by 429
A comparative study of machine learning classifiers for modeling travel mode choice
Hagenauer, J., & Helbich, M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.057 · Cited by 424
Bagging and Boosting Classification Trees to Predict Churn
Lemmens, A., & Croux, C. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.2.276 · Cited by 411
The Practical Implementation of Bayesian Model Selection
Chipman, H., George, E. I., & McCulloch, R. E. (2001) · Institute of Mathematical Statistics Lecture Notes - Monograph Series · doi:10.1214/lnms/1215540964 · Cited by 416
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
Ye, L., & Keogh, E. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0179-5 · Cited by 406
Evaluating Prediction Rules for t -Year Survivors With Censored Regression Models
Uno, H., Cai, T., Tian, L., & Wei, L. J. (2007) · Journal of the American Statistical Association · doi:10.1198/016214507000000149 · Cited by 413
Methotrexate-Induced Neurotoxicity and Leukoencephalopathy in Childhood Acute Lymphoblastic Leukemia
Bhojwani, D., Sabin, N. D., Pei, D., Yang, J. J., Khan, R. B., Panetta, J. C., Krull, K. R., Inaba, H., Rubnitz, J. E., Metzger, M. L., et al. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.53.0808 · Cited by 400
Business intelligence: data mining and optimization for decision making
Vercellis, C., Ahmadi, A., & Mohebi, A. (2016) · doi:10.5281/zenodo.13983826 · Cited by 398
Bayesian approach for neural networks—review and case studies
Lampinen, J., & Vehtari, A. (2001) · Neural Networks · doi:10.1016/s0893-6080(00)00098-8 · Cited by 392
Identification of Individuals With Insulin Resistance Using Routine Clinical Measurements
Stern, S. E., Williams, K., Ferrannini, E., DeFronzo, R. A., Bogardus, C., & Stern, M. P. (2005) · Diabetes · doi:10.2337/diabetes.54.2.333 · Cited by 384
Incremental on-line learning: A review and comparison of state of the art algorithms
Losing, V., Hammer, B., & Wersing, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.06.084 · Cited by 382
Cawthon, P. M., Peters, K. W., Shardell, M. D., McLean, R. R., Dam, T.-T. L., Kenny, A. M., Fragala, M. S., Harris, T. B., Kiel, D. P., Guralnik, J. M., et al. (2014) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glu023 · Cited by 388
Data mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
Roberts, J. A., Abdul-Aziz, M.-H., Davis, J. S., Dulhunty, J. M., Cotta, M. O., Myburgh, J., Bellomo, R., & Lipman, J. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201601-0024oc · Cited by 379
Using Model Trees for Classification
Frank, E., Wang, Y., Inglis, S., Holmes, G., & Witten, I. H. (1998) · Machine Learning · doi:10.1023/a:1007421302149 · Cited by 377
The case for the inclusion of female subjects in basic science studies of pain
Mogil, J. S., & Chanda, M. L. (2005) · Pain · doi:10.1016/j.pain.2005.06.020 · Cited by 376
Freimer, N., & Sabatti, C. (2003) · Nature Genetics · doi:10.1038/ng0503-15 · Cited by 372
Relational Reinforcement Learning
Džeroski, S., De Raedt, L., & Driessens, K. (2001) · Machine Learning · doi:10.1023/a:1007694015589 · Cited by 371
Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus
Barakat, N., Bradley, A. P., & Barakat, M. N. H. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2039485 · Cited by 367
Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017) · Big Data Research · doi:10.1016/j.bdr.2017.07.003 · Cited by 367
Series in Machine Perception and Artificial Intelligence
(2012) · Series in machine perception and artificial intelligence · doi:10.1142/smpai · Cited by 365
Defining insulin resistance from hyperinsulinemic-euglycemic clamps
Tam, C. S., Xie, W., Johnson, W. D., Cefalu, W. T., Redman, L. M., & Ravussin, E. (2012) · Diabetes Care · doi:10.2337/dc11-2339 · Cited by 364
Multiple Imputation for Missing Data via Sequential Regression Trees
Burgette, L. F., & Reiter, J. P. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq260 · Cited by 363
Conditional random fields for activity recognition
Vail, D. L., Veloso, M. M., & Lafferty, J. D. (2007) · Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1329125.1329409 · Cited by 357
Short-Term Prediction of Wind Farm Power: A Data Mining Approach
Kusiak, A., Zheng, H., & Song, Z. (2009) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2008.2006552 · Cited by 339
Chesler, E. J., Wilson, S. G., Lariviere, W. R., Rodriguez-Zas, S. L., & Mogil, J. S. (2002) · Neuroscience & Biobehavioral Reviews · doi:10.1016/s0149-7634(02)00103-3 · Cited by 331
Human Activity Helps Prey Win the Predator-Prey Space Race
Muhly, T. B., Semeniuk, C., Massolo, A., Hickman, L., & Musiani, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017050 · Cited by 337
A Pearson’s correlation coefficient based decision tree and its parallel implementation
Mu, Y., Liu, X., & Wang, L. (2018) · Information Sciences · doi:10.1016/j.ins.2017.12.059 · Cited by 336
Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.5960 · Cited by 333
The Effects of Data Quality on Machine Learning Performance on Tabular Data
Mohammed, S., Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Naumann, F., & Harmouch, H. (2025) · Information Systems · doi:10.1016/j.is.2025.102549 · Cited by 331
A Survey of Systemic Risk Analytics
Bisias, D., Flood, M. D., Lo, A. W., & Valavanis, S. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.1983602 · Cited by 331
On the use of MapReduce for imbalanced big data using Random Forest
del Río, S., López, V., Benítez, J. M., & Herrera, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.03.043 · Cited by 328
Machine learning, statistical learning and the future of biological research in psychiatry
Iniesta, R., Stahl, D., & McGuffin, P. (2016) · Psychological Medicine · doi:10.1017/s0033291716001367 · Cited by 328
Senoner, J., Netland, T., & Feuerriegel, S. (2022) · Management Science · doi:10.1287/mnsc.2021.4190 · Cited by 317
Iterative random forests to discover predictive and stable high-order interactions
Basu, S., Kumbier, K., Brown, J. B., & Yu, B. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711236115 · Cited by 317
Deep learning algorithm predicts diabetic retinopathy progression in individual patients
Arcadu, F., Benmansour, F., Maunz, A., Willis, J., Haskova, Z., & Prunotto, M. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0172-3 · Cited by 305
An empirical study of machine learning techniques for affect recognition in human–robot interaction
Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0025-y · Cited by 313
Nigrovic, L. E. (2007) · JAMA · doi:10.1001/jama.297.1.52 · Cited by 306
Fungal cell gigantism during mammalian infection
Zaragoza, O., García-Rodas, R., Nosanchuk, J. D., Cuenca-Estrella, M., Rodríguez-Tudela, J. L., & Casadevall, A. (2010) · PLoS Pathogens · doi:10.1371/journal.ppat.1000945 · Cited by 311
Metadata
- Authors
- Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1984-09
- Volume
- 40
- Issue
- 3
- Pages
- 874
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W1594031697
- Cited by
- 23,522
- References
- 15
- FWCI
- 62.48
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.