Random Forests

Cited by (1,834)

Items 601–700 of 1,834

1. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
   Yaping Cai, Kaiyu Guan, David B. Lobell, Andries B. Potgieter, Shaowen Wang, Jian Peng, Tianfang Xu, Senthold Asseng, Yongguang Zhang, Liangzhi You et al. · 2019 · Agricultural and Forest Meteorology
2. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research
   Reza Khatami, Giorgos Mountrakis, Stephen V. Stehman · 2016 · Remote Sensing of Environment
3. A review of surrogate models and their application to groundwater modeling
   M. J. Asher, Barry Croke, Anthony J. Jakeman, Luk J. M. Peeters · 2015 · Water Resources Research
4. An improved random forest based on the classification accuracy and correlation measurement of decision trees
   Zhigang Sun, Guotao Wang, Pengfei Li, Hui Wang, Zhang Min, Xiaowen Liang · 2023 · Expert Systems with Applications
5. Weed detection in soybean crops using ConvNets
   Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes · 2017 · Computers and Electronics in Agriculture
6. Pedotransfer Functions in Earth System Science: Challenges and Perspectives
   Kris Van Looy, Johan Bouma, M. Herbst, John Koestel, Budiman Minasny, Umakant Mishra, Carsten Montzka, Attila Nemes, Yakov A. Pachepsky, José Padarian et al. · 2017 · Reviews of Geophysics
7. A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities
   Sergio González, Salvador García, Javier Del Ser, Lior Rokach, Francisco Herrera · 2020 · Information Fusion
8. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
   Victor Francisco Rodriguez-Galiano, Mario Chica‐Olmo, Francisco Abarca-Hernández, Peter M. Atkinson, C. Jeganathan · 2012 · Remote Sensing of Environment
9. Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines
   Jennifer E. Spindel, Hasina Begum, Deniz Akdemir, Parminder S. Virk, B. C. Y. Collard, Edilberto D. Redoña, G. N. Atlin, Jean‐Luc Jannink, Susan R. McCouch · 2015 · PLoS Genetics
10. CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
    João Carreira, Cristian Sminchisescu · 2011 · IEEE Transactions on Pattern Analysis and Machine Intelligence
11. Subsistence strategies in traditional societies distinguish gut microbiomes
    Obregon-Tito, Alexandra J., Tito, Raul Y., Metcalf, Jessica, Sankaranarayanan, Krithivasan, Clemente, Jose C., Ursell, Luke K., Zech Xu, Zhenjiang, Van Treuren, Will, Knight, Rob, Gaffney, Patrick M. et al. · 2015 · Nature Communications
12. Random-Forests-Based Network Intrusion Detection Systems
    Jiong Zhang, Mohammad Zulkernine, Anwar Haque · 2008 · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
13. Literature review: Machine learning techniques applied to financial market prediction
    Bruno Miranda Henrique, Vinícius Amorim Sobreiro, Herbert Kimura · 2019 · Expert Systems with Applications
14. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
    Mayr, Andreas, Klambauer, Günter, Unterthiner, Thomas, Steijaert, Marvin, Wegner, Jörg K., Ceulemans, Hugo, Clevert, Djork-Arné, Hochreiter, Sepp · 2018 · Chemical Science
15. Integrating abundance and functional traits reveals new global hotspots of fish diversity
    Rick D. Stuart‐Smith, Amanda E. Bates, Jonathan S. Lefcheck, J. Emmett Duffy, Susan C. Baker, Russell J. Thomson, Jemina Stuart‐Smith, Nicole A. Hill, Stuart Kininmonth, Laura Airoldi et al. · 2013 · Nature
16. Hough Forests for Object Detection, Tracking, and Action Recognition
    Jüergen Gall, Angela Yao, Nima Razavi, Luc Van Gool, Victor Lempitsky · 2011 · IEEE Transactions on Pattern Analysis and Machine Intelligence
17. A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost
    Dahai Zhang, Liyang Qian, Baijin Mao, Can Huang, Bin Huang, Yulin Si · 2018 · IEEE Access
18. Flood susceptibility modelling using advanced ensemble machine learning models
    Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sonali Kundu, Kutub Uddin Eibek, Quoc Bao Pham, Alban Kuriqi, Nguyễn Thị Thùy Linh · 2020 · Geoscience Frontiers
19. A new method for detecting and interpreting biodiversity and ecological community thresholds: <i>Threshold Indicator Taxa ANalysis (TITAN)</i>
    Baker, Matthew E., King, Ryan S. · 2010 · Methods in Ecology and Evolution
20. A Survey on Ensemble Learning for Data Stream Classification
    Gomes, Heitor Murilo, Barddal, Jean Paul, Enembreck, Fabrício, Bifet, Albert · 2018 · ACM Computing Surveys
21. Efficient Human Pose Estimation from Single Depth Images
    Jamie Shotton, Ross Girshick, Andrew W Fitzgibbon, Toby Sharp, Mat Cook, Mark Finocchio, Richard Moore, Pushmeet Kohli, Antonio Criminisi, Alex Kipman et al. · 2012 · IEEE Transactions on Pattern Analysis and Machine Intelligence
22. Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning
    Amy McGovern, Ryan Lagerquist, David John Gagne, G. Eli Jergensen, Kimberly L. Elmore, Cameron R. Homeyer, Travis M. Smith · 2019 · Bulletin of the American Meteorological Society
23. Identification, analysis, and prediction of protein ubiquitination sites
    Predrag Radivojac A, Vladimir Vacic, Chad A. Haynes, Ross Cocklin, Amrita Mohan, Joshua W. Heyen, Mark G. Goebl, Lilia M. Iakoucheva · 2009 · Proteins Structure Function and Bioinformatics
24. Estimating Treatment Effects with Causal Forests: An Application
    Susan Carleton Athey, Stefan Wager · 2019 · Observational Studies
25. Predicting RNA-Protein Interactions Using Only Sequence Information
    Usha K. Muppirala, Vasant Honavar, Drena L Dobbs · 2011 · BMC Bioinformatics
26. PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies
    Sajia Akhter, Ramy Karam Aziz, Robert Alan Edwards · 2012 · Nucleic Acids Research
27. An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
    Tantithamthavorn, Chakkrit, McIntosh, Shane, Hassan, Ahmed E., Matsumoto, Kenichi · 2017 · IEEE Transactions on Software Engineering
28. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
    Jin-Long Wu, Heng Xiao, Eric Paterson · 2018 · Physical Review Fluids
29. Random Forests for Real Time 3D Face Analysis
    Gabriele Fanelli, Matthias Dantone, Jüergen Gall, Andrea Fossati, Luc Van Gool · 2012 · International Journal of Computer Vision
30. An overview of variable selection methods in multivariate analysis of near-infrared spectra
    Yong‐Huan Yun, Hong‐Dong Li, Baichuan Deng, Dongsheng Cao · 2019 · TrAC Trends in Analytical Chemistry
31. Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective
    Sarker, Iqbal H. · 2021 · SN Computer Science
32. Measuring biological age using omics data
    Rutledge, Jarod, Oh, Hamilton, Wyss-Coray, Tony · 2022 · Nature Reviews Genetics
33. An Efficient Explanation of Individual Classifications using Game Theory
    Erik Štrumbelj, Igor Kononenko · 2010 · Repository of the University of Ljubljana (University of Ljubljana)
34. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling
    Stefanos Georganos, Taïs Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine G. Vanhuysse, Nicholus Mboga, Eléonore Wolff A, Stamatis Kalogirou · 2019 · Geocarto International
35. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
    Cheng Fan, Fu Xiao, Shengwei Wang · 2014 · Applied Energy
36. Logistic regression was as good as machine learning for predicting major chronic diseases
    Simon Nusinovici, Yih Chung Tham, Marco Yu Chak Yan, Daniel Shu Wei Ting, Jialiang Li, Charumathi Sabanayagam, Tien Yin Wong, Ching‐Yu Cheng · 2020 · Journal of Clinical Epidemiology
37. Using Class Imbalance Learning for Software Defect Prediction
    Shuo Wang, Xin Yao · 2013 · IEEE Transactions on Reliability
38. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis
    Quanlong Feng, Jiantao Liu, Jianhua Gong · 2015 · Remote Sensing
39. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA
    Marwa Waseem A. Halmy, Paul E. Gessler, Jeffrey A. Hicke, Boshra B. Salem · 2015 · Applied Geography
40. Identifying Mendelian disease genes with the Variant Effect Scoring Tool
    Hannah K. Carter, Christopher Blair Douville, Peter D. Stenson, D.N. Cooper, Rachel Karchin · 2013 · BMC Genomics
41. The maternal microbiome modulates fetal neurodevelopment in mice
    Vuong, Helen E., Pronovost, Geoffrey N., Williams, Drake W., Coley, Elena J. L., Siegler, Emily L., Qiu, Austin, Kazantsev, Maria, Wilson, Chantel J., Rendon, Tomiko, Hsiao, Elaine Y. · 2020 · Nature
42. Contextual classification of lidar data and building object detection in urban areas
    Joachim Niemeyer, Franz Rottensteiner, Uwe Soergel · 2013 · ISPRS Journal of Photogrammetry and Remote Sensing
43. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
    Guomin Huang, Lifeng Wu, Xin Ma, Weiqiang Zhang, Junliang Fan, Xiang Jun Yu, Wenzhi Zeng, Hanmi Zhou · 2019 · Journal of Hydrology
44. Handling class imbalance in customer churn prediction
    Jonathan Burez, Dirk Van den Poel · 2008 · Expert Systems with Applications
45. Semi-Automatic Classification Plugin: A Python tool for the download and processing of remote sensing images in QGIS
    Luca Congedo · 2021 · The Journal of Open Source Software
46. Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China
    Junliang Fan, Wenjun Yue, Lifeng Wu, Fucang Zhang, Huanjie Cai, Xiukang Wang, Xianghui Lu, Youzhen Xiang · 2018 · Agricultural and Forest Meteorology
47. Machine learning for email spam filtering: review, approaches and open research problems
    E. Dada, Joseph Stephen Bassi, H. Chiroma, S. Abdulhamid, A. Adetunmbi, Opeyemi Ajibuwa · 2019 · Heliyon
48. Machine learning applications for building structural design and performance assessment: State-of-the-art review
    Han Sun, H. Burton, Honglan Huang · 2021 · Journal of Building Engineering
49. A general approach for predicting the behavior of the Supreme Court of the United States
    Daniel Martin Katz, Michael James Bommarito, Josh Blackman · 2017 · PLoS ONE
50. Big Data Integration
    Dong Xin, Divesh Srivastava · 2013 · Synthesis lectures on data management
51. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods
    Juan Eugenio Iglesias, Chengyi Liu, Paul M. Thompson, Zhuowen Tu · 2011 · IEEE Transactions on Medical Imaging
52. Mapping the forest disturbance regimes of Europe
    Cornelius Senf, Rupert Seidl · 2020 · Nature Sustainability
53. Class-Specific Hough Forests for Object Detection
    Jüergen Gall, Victor Lempitsky · 2009 · Advances in computer vision and pattern recognition
54. Using data mining to predict secondary school student performance
    Paulo Cesar Cortez, Alice Maria Gonçalves Silva · 2008 · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
55. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series
    Stephan Estel, Tobias Kuemmerle, Camilo Alcántara, Christian Levers, Alexander V. Prishchepov, Patrick Hostert · 2015 · Remote Sensing of Environment
56. Digital zooplankton image analysis using the ZooScan integrated system
    Gabriel Gorsky, Mark D. Ohman, Marc Picheral, Stéphane Gasparini, Lars Stemmann, Jean‐Baptiste Romagnan, Alison M. Cawood, Stéphane Pesant, Carmen García‐Comas, F. Prejger · 2010 · Journal of Plankton Research
57. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
    Juan Eugenio Iglesias, Ricardo Insausti, Garikoitz Lerma‐Usabiaga, Martina Bocchetta, Koen Van Leemput, Douglas N. Greve, Andre J. W. van der Kouwe, Bruce R. Fischl, César Caballero‐Gaudes, Pedro M. Paz‐Alonso · 2018 · NeuroImage
58. Nanoparticle synthesis assisted by machine learning
    Huachen Tao, Tianyi Wu, Matteo Aldeghi, Tony Wu, Alán Aspuru‐Guzik, Eugenia Kumacheva · 2021 · Nature Reviews Materials
59. Cell-of-origin chromatin organization shapes the mutational landscape of cancer
    Polak, Paz, Karlić, Rosa, Koren, Amnon, Thurman, Robert, Sandstrom, Richard, Lawrence, Michael S., Reynolds, Alex, Rynes, Eric, Vlahoviček, Kristian, Stamatoyannopoulos, John A. et al. · 2015 · Nature
60. Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
    Jingjing Wang, Chunxiao Jiang, Haijun Zhang, Yong Ren, Kwang-Cheng Chen, Lajos Hanzo · 2020 · IEEE Communications Surveys & Tutorials
61. Result Analysis of the NIPS 2003 Feature Selection Challenge
    Isabelle Guyon, Steve Gunn, Asa Ben‐Hur, Gideon Dror · 2004
62. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery
    Peng Fu, Qihao Weng · 2016 · Remote Sensing of Environment
63. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
    Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun · 2018 · Computers and Electronics in Agriculture
64. Opening the black box: the promise and limitations of explainable machine learning in cardiology
    Petch, Jeremy, Di, Shuang, Nelson, Walter · 2022 · Canadian Journal of Cardiology
65. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges
    Benjamin Alan Goldstein, Ann Marie Návar, Rickey E. Carter · 2016 · European Heart Journal
66. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping
    Patrick Griffiths, Claas Nendel, Patrick Hostert · 2018 · Remote Sensing of Environment
67. Predictably Unequal? The Effects of Machine Learning on Credit Markets
    FUSTER, ANDREAS, GOLDSMITH‐PINKHAM, PAUL, RAMADORAI, TARUN, WALTHER, ANSGAR · 2022 · The Journal of Finance
68. Predicting students' final performance from participation in on-line discussion forums
    Cristóbal Romero, Manuel-Ignacio López, José María Luna, Sebastián Ventura · 2013 · Computers & Education
69. Intelligible models for classification and regression
    Lou, Yin, Caruana, Rich, Gehrke, Johannes · 2012 · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
70. Significant increase in natural disturbance impacts on European forests since 1950
    Marco Patacca, Marcus Lindner, Manuel Esteban Lucas‐Borja, Thomas Cordonnier, Gal Fidej, Barry Gardiner, Ylva Hauf, Gediminas Jasinevičius, Sophie Labonne, Edgaras Linkevičius et al. · 2022 · Global Change Biology
71. Estimating individual treatment effect: generalization bounds and algorithms
    Uri Shalit, Fredrik Johansson, David Alexander Sontag · 2016 · arXiv (Cornell University)
72. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches
    Scott L. Powell, Warren B. Cohen, Sean P. Healey, Robert E. Kennedy, Gretchen G. Moisen, Kenneth B. Pierce, Janet L. Ohmann · 2010 · Remote Sensing of Environment
73. A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform
    Pardhasaradhi G. Teluguntla, Prasad S. Thenkabail, Adam J. Oliphant, Jun Xiong, Murali Krishna Gumma, Russell G. Congalton, Kamini Yadav, ALFREDO R. HUETE · 2018 · ISPRS Journal of Photogrammetry and Remote Sensing
74. Developing clinical prediction models: a step-by-step guide
    Efthimiou, Orestis, Seo, Michael, Chalkou, Konstantina, Debray, Thomas, Egger, Matthias, Salanti, Georgia · 2024 · BMJ
75. Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
    Sheridan, Robert P., Wang, Wei Min, Liaw, Andy, Ma, Junshui, Gifford, Eric M. · 2016 · Journal of Chemical Information and Modeling
76. An Introduction to Feature Extraction
    Isabelle Guyon, André Elisseeff · 2008 · Studies in fuzziness and soft computing
77. Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems
    Elisabetta Chicca, Fabio Stefanini, Chiara Bartolozzi, Giacomo Indiveri · 2014 · Proceedings of the IEEE
78. Machine learning advances for time series forecasting
    Ricardo Masini, Marcelo Cunha Medeiros, Eduardo F. Mendes · 2021 · Journal of Economic Surveys
79. Gradient forests: calculating importance gradients on physical predictors
    Nick Ellis, Stephen J. Smith, C. Roland Pitcher · 2011 · Ecology
80. Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
    Michael P. Menden, Francesco Iorio, Mathew J. Garnett, Ultan McDermott, Cyril H. Benes, Pedro J. Ballester, Julio Sáez-Rodríguez · 2013 · PLoS ONE
81. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
    Martin Vallières, Emily Kay‐Rivest, Léo Jean Perrin, X. Liem, C Furstoss, Hugo J.W.L. Aerts, Nader Khaouam, Phuc Félix Nguyen‐Tan, Chang-Shu Wang, Khalil Sultanem et al. · 2017 · Scientific Reports
82. Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict
    Monroe, Burt L., Colaresi, Michael P., Quinn, Kevin M. · 2008 · Political Analysis
83. Opposing Functions of Interferon Coordinate Adaptive and Innate Immune Responses to Cancer Immune Checkpoint Blockade
    Benci, Joseph L., Johnson, Lexus R., Choa, Ruth, Xu, Yuanming, Qiu, Jingya, Zhou, Zilu, Xu, Bihui, Ye, Darwin, Nathanson, Katherine L., June, Carl H. et al. · 2019 · Cell
84. Subgroup identification from randomized clinical trial data
    Jared C. Foster, Jeremy M. G. Taylor, Stephen J. Ruberg · 2011 · Statistics in Medicine
85. Unsupervised Learning With Random Forest Predictors
    Tao Shi, Steve Horvath · 2006 · Journal of Computational and Graphical Statistics
86. CIFAR10-DVS: An Event-Stream Dataset for Object Classification
    Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, Luping Shi · 2017 · Frontiers in Neuroscience
87. Macroevolutionary convergence connects morphological form to ecological function in birds
    Alex L. Pigot, Catherine Sheard, Eliot T. Miller, Tom P. Bregman, Benjamin G. Freeman, Uri Roll, Nathalie Seddon A, Christopher H. Trisos, Brian C. Weeks, Joseph A. Tobias · 2020 · Nature Ecology & Evolution
88. BagBoosting for tumor classification with gene expression data
    Marcel Dettling · 2004 · Bioinformatics
89. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach
    Jing Wei, Wei Huang, Zhanqing Li, Wenhao Xue, Yiran Peng, Lin Sun, Maureen C. Cribb · 2019 · Remote Sensing of Environment
90. Multiple dynamic representations in the motor cortex during sensorimotor learning
    Huber, D., Gutnisky, D. A., Peron, S., O’Connor, D. H., Wiegert, J. S., Tian, L., Oertner, T. G., Looger, L. L., Svoboda, K. · 2012 · Nature
91. Combining unsupervised and supervised learning in credit card fraud detection
    Fabrizio Carcillo, Yann‐Aël Le Borgne, Olivier Caelen, Yacine Kessaci, Frédéric Oblé, Gianluca Bontempi · 2019 · Information Sciences
92. Sarcoma classification by DNA methylation profiling
    C. Koelsche, D. Schrimpf, D. Stichel, M. Sill, F. Sahm, D. Reuss, Mirjam Blattner, Barbara C. Worst, C. Heilig, K. Beck et al. · 2021 · Nature Communications
93. Classification of time series by shapelet transformation
    Hills, Jon, Lines, Jason, Baranauskas, Edgaras, Mapp, James, Bagnall, Anthony · 2014 · Data Mining and Knowledge Discovery
94. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
    Angermueller, Christof, Lee, Heather J., Reik, Wolf, Stegle, Oliver · 2017 · Genome Biology
95. Association Between Older Age and More Successful Aging: Critical Role of Resilience and Depression
    Dilip V. Jeste, Gauri Nayak Savla, Wesley Kurt Thompson, Ipsit Vihang Vahia, Danielle Glorioso, Averria Sirkin Martin, Barton W. Palmer, David Rock, Shahrokh Golshan, HELENA CHMURA KRAEMER et al. · 2012 · American Journal of Psychiatry
96. A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees
    Arno De Caigny, Kristof Coussement, Koen W. De Bock · 2018 · European Journal of Operational Research
97. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods
    Jochem Verrelst, Zbyněk Malenovský, Christiaan van der Tol, Gustau Camps‐Valls, Jean‐Philippe Gastellu‐Etchegorry, P. Lewis, Peter R. J. North, J. Moreno · 2018 · Surveys in Geophysics
98. deepDR: a network-based deep learning approach toin silicodrug repositioning
    Xiangxiang Zeng, Siyi Zhu, Xiangrong Liu, Yadi Zhou, Ruth Nussinov, Feixiong Cheng · 2019 · Bioinformatics
99. In silico prediction of splice-altering single nucleotide variants in the human genome
    X. Jian, E. Boerwinkle, Xiao-Ming Liu · 2014 · Nucleic Acids Research
100. An empirical evaluation of supervised learning in high dimensions
     Rich Caruana, Nikos Karampatziakis, Ainur Yessenalina · 2008 · International Conference on Machine Learning