Bagging predictors

Cited by (228)

Items 101–200 of 228

1. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
   Schwarz, Daniel F., König, Inke R., Ziegler, Andreas · 2010 · Bioinformatics
2. A review of Earth Artificial Intelligence
   Sun, Ziheng, Sandoval, Laura, Crystal-Ornelas, Robert, Mousavi, S. Mostafa, Wang, Jinbo, Lin, Cindy, Cristea, Nicoleta, Tong, Daniel, Carande, Wendy Hawley, Ma, Xiaogang et al. · 2022 · Computers & Geosciences
3. Mining opinions from instructor evaluation reviews: a deep learning approach
   Onan, Aytuğ · 2020 · Computer Applications in Engineering Education
4. Beating the hold-out: bounds for K-fold and progressive cross-validation
   Blum, Avrim, Kalai, Adam, Langford, John · 1999 · Proceedings of the twelfth annual conference on Computational learning theory
5. Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree
   Azam, Zahedi, Islam, Md. Motaharul, Huda, Mohammad Nurul · 2023 · IEEE Access
6. Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach
   Chen, Xiqun (Michael), Zahiri, Majid, Zhang, Shuaichao · 2017 · Transportation Research Part C: Emerging Technologies
7. Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?
   de Laat, Paul B. · 2018 · Philosophy & Technology
8. How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes]
   Akhtar, Md Shad, Ekbal, Asif, Cambria, Erik · 2020 · IEEE Computational Intelligence Magazine
9. An effective approach for software project effort and duration estimation with machine learning algorithms
   Pospieszny, Przemyslaw, Czarnacka-Chrobot, Beata, Kobylinski, Andrzej · 2018 · Journal of Systems and Software
10. Combinations of biomarkers predictive of later life mortality
    Gruenewald, Tara L., Seeman, Teresa E., Ryff, Carol D., Karlamangla, Arun S., Singer, Burton H. · 2006 · Proceedings of the National Academy of Sciences
11. A quantitative uncertainty metric controls error in neural network-driven chemical discovery
    Janet, Jon Paul, Duan, Chenru, Yang, Tzuhsiung, Nandy, Aditya, Kulik, Heather J. · 2019 · Chemical Science
12. Decision tree classifiers for automated medical diagnosis
    Azar, Ahmad Taher, El-Metwally, Shereen M. · 2013 · Neural Computing and Applications
13. Should lung transplantation be performed for patients on mechanical respiratory support? The US experience
    Mason, David P., Thuita, Lucy, Nowicki, Edward R., Murthy, Sudish C., Pettersson, Gösta B., Blackstone, Eugene H. · 2010 · The Journal of Thoracic and Cardiovascular Surgery
14. Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit
    Camps, Julià, Samà, Albert, Martín, Mario, Rodríguez-Martín, Daniel, Pérez-López, Carlos, Moreno Arostegui, Joan M., Cabestany, Joan, Català, Andreu, Alcaine, Sheila, Mestre, Berta et al. · 2018 · Knowledge-Based Systems
15. Casting out Demons: Sanitizing Training Data for Anomaly Sensors
    Cretu, Gabriela F., Stavrou, Angelos, Locasto, Michael E., Stolfo, Salvatore J., Keromytis, Angelos D. · 2008 · 2008 IEEE Symposium on Security and Privacy (sp 2008)
16. Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
    Bhatt, Umang, Antorán, Javier, Zhang, Yunfeng, Liao, Q. Vera, Sattigeri, Prasanna, Fogliato, Riccardo, Melançon, Gabrielle, Krishnan, Ranganath, Stanley, Jason, Tickoo, Omesh et al. · 2021 · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
17. Authorship Attribution for Social Media Forensics
    Rocha, Anderson, Scheirer, Walter J., Forstall, Christopher W., Cavalcante, Thiago, Theophilo, Antonio, Shen, Bingyu, Carvalho, Ariadne R. B., Stamatatos, Efstathios · 2017 · IEEE Transactions on Information Forensics and Security
18. Aortic root replacement with cryopreserved allograft for prosthetic valve endocarditis
    Sabik, Joseph F., Lytle, Bruce W., Blackstone, Eugene H., Marullo, Antonino G.M., Pettersson, Gosta B., Cosgrove, Delos M. · 2002 · The Annals of Thoracic Surgery
19. An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance
    Chen, Zhuo, Hu, Wenlu, Wang, Junjue, Zhao, Siyan, Amos, Brandon, Wu, Guanhang, Ha, Kiryong, Elgazzar, Khalid, Pillai, Padmanabhan, Klatzky, Roberta et al. · 2017 · Proceedings of the Second ACM/IEEE Symposium on Edge Computing
20. Random forest regression for magnetic resonance image synthesis
    Jog, Amod, Carass, Aaron, Roy, Snehashis, Pham, Dzung L., Prince, Jerry L. · 2017 · Medical Image Analysis
21. Survival after valve replacement for aortic stenosis: Implications for decision making
    Mihaljevic, Tomislav, Nowicki, Edward R., Rajeswaran, Jeevanantham, Blackstone, Eugene H., Lagazzi, Luigi, Thomas, James, Lytle, Bruce W., Cosgrove, Delos M. · 2008 · The Journal of Thoracic and Cardiovascular Surgery
22. An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor
    Sharif, Muhammad, Amin, Javaria, Raza, Mudassar, Yasmin, Mussarat, Satapathy, Suresh Chandra · 2020 · Pattern Recognition Letters
23. Surgery for permanent atrial fibrillation: impact of patient factors and lesion set
    Gillinov, A. Marc, Bhavani, Sekar, Blackstone, Eugene H., Rajeswaran, Jeevanantham, Svensson, Lars G., Navia, Jose L., Pettersson, B.Gösta, Sabik, Joseph F., Smedira, Nicholas G., Mihaljevic, Tomislav et al. · 2006 · The Annals of Thoracic Surgery
24. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms
    Erdal, Halil Ibrahim, Karakurt, Onur · 2013 · Journal of Hydrology
25. Improving propensity score estimators’ robustness to model misspecification using super learner.
    Pirracchio, Romain, Petersen, Maya L., van der Laan, Mark · 2015 · American Journal of Epidemiology
26. Explainable decision forest: Transforming a decision forest into an interpretable tree
    Sagi, Omer, Rokach, Lior · 2020 · Information Fusion
27. Mortality Risk Score Prediction in an Elderly Population Using Machine Learning
    Rose, Sherri · 2013 · American Journal of Epidemiology
28. Forecasting Murder Within a Population of Probationers and Parolees: A High Stakes Application of Statistical Learning
    Berk, Richard, Sherman, Lawrence, Barnes, Geoffrey, Kurtz, Ellen, Ahlman, Lindsay · 2009 · Journal of the Royal Statistical Society Series A: Statistics in Society
29. Brain–phenotype models fail for individuals who defy sample stereotypes
    Greene, Abigail S., Shen, Xilin, Noble, Stephanie, Horien, Corey, Hahn, C. Alice, Arora, Jagriti, Tokoglu, Fuyuze, Spann, Marisa N., Carrión, Carmen I., Barron, Daniel S. et al. · 2022 · Nature
30. Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling
    Guleria, Pratiyush, Sood, Manu · 2023 · Education and Information Technologies
31. Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions
    Jun Zhang, Chao Chen, Yang Xiang, Wanlei Zhou, Yong Xiang · 2013 · IEEE Transactions on Information Forensics and Security
32. Social network analysis for customer churn prediction
    Verbeke, Wouter, Martens, David, Baesens, Bart · 2014 · Applied Soft Computing
33. Continued Intention to Use of M-Banking in Jordan by Integrating UTAUT, TPB, TAM and Service Quality with ML
    Abu-Taieh, Evon M., AlHadid, Issam, Abu-Tayeh, Sabah, Masa’deh, Ra’ed, Alkhawaldeh, Rami S., Khwaldeh, Sufian, Alrowwad, Ala’aldin · 2022 · Journal of Open Innovation: Technology, Market, and Complexity
34. Automated trading with performance weighted random forests and seasonality
    Booth, Ash, Gerding, Enrico, McGroarty, Frank · 2014 · Expert Systems with Applications
35. Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
    Zhu, Shilin, Dong, Xin, Su, Hao · 2019 · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
36. The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
    Watson, David · 2019 · Minds and Machines
37. Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
    Maier, Oskar, Schröder, Christoph, Forkert, Nils Daniel, Martinetz, Thomas, Handels, Heinz · 2015 · PLOS ONE
38. Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
    Adler, Afek Ilay, Painsky, Amichai · 2022 · Entropy
39. Forward Noise Adjustment Scheme for Data Augmentation
    Moreno-Barea, Francisco J., Strazzera, Fiammetta, Jerez, Jose M., Urda, Daniel, Franco, Leonardo · 2018 · 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
40. Modeling CLV: A test of competing models in the insurance industry
    Donkers, Bas, Verhoef, Peter C., de Jong, Martijn G. · 2007 · Quantitative Marketing and Economics
41. Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
    Fluke, Christopher J., Jacobs, Colin · 2020 · WIREs Data Mining and Knowledge Discovery
42. An experimental evaluation of ensemble methods for EEG signal classification
    Sun, Shiliang, Zhang, Changshui, Zhang, Dan · 2007 · Pattern Recognition Letters
43. Network inference with Granger causality ensembles on single-cell transcriptomics
    Deshpande, Atul, Chu, Li-Fang, Stewart, Ron, Gitter, Anthony · 2022 · Cell Reports
44. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging
    Eloyan, Ani, Muschelli, John, Nebel, Mary Beth, Liu, Han, Han, Fang, Zhao, Tuo, Barber, Anita D., Joel, Suresh, Pekar, James J., Mostofsky, Stewart H. et al. · 2012 · Frontiers in Systems Neuroscience
45. CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks
    Sarker, Iqbal H. · 2021 · Internet of Things
46. Patient length of stay and mortality prediction: A survey
    Awad, Aya, Bader–El–Den, Mohamed, McNicholas, James · 2017 · Health Services Management Research
47. Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques
    Knudby, Anders, LeDrew, Ellsworth, Brenning, Alexander · 2010 · Remote Sensing of Environment
48. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease
    the Alzheimer’s Disease Neuroimaging Initiative, Habeck, Christian, Stern, Yaakov · 2010 · Cell Biochemistry and Biophysics
49. Predicting student dropout in subscription-based online learning environments: the beneficial impact of the logit leaf model
    Coussement, Kristof, Phan, Minh, De Caigny, Arno, Benoit, Dries F., Raes, Annelies · 2020 · Decision Support Systems
50. Flexible neural trees ensemble for stock index modeling
    Chen, Yuehui, Yang, Bo, Abraham, Ajith · 2007 · Neurocomputing
51. A Survey on Differentially Private Machine Learning [Review Article]
    Gong, Maoguo, Xie, Yu, Pan, Ke, Feng, Kaiyuan, Qin, A.K. · 2020 · IEEE Computational Intelligence Magazine
52. A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics
    Devriendt, Floris, Moldovan, Darie, Verbeke, Wouter · 2018 · Big Data
53. Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood
    Schultebraucks, Katharina, Yadav, Vijay, Shalev, Arieh Y., Bonanno, George A., Galatzer-Levy, Isaac R. · 2022 · Psychological Medicine
54. Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods
    Henckaerts, Roel, Côté, Marie-Pier, Antonio, Katrien, Verbelen, Roel · 2021 · North American Actuarial Journal
55. An Analysis of ISO 26262: Machine Learning and Safety in Automotive Software
    Salay, Rick, Queiroz, Rodrigo, Czarnecki, Krzysztof · 2018 · SAE Technical Paper Series
56. The Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD): A Screening Tool for Depression
    Gibbons, Robert D., Hooker, Giles, Finkelman, Matthew D., Weiss, David J., Pilkonis, Paul A., Frank, Ellen, Moore, Tara, Kupfer, David J. · 2013 · The Journal of Clinical Psychiatry
57. Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory
    Kruppa, Jochen, Liu, Yufeng, Biau, Gérard, Kohler, Michael, König, Inke R., Malley, James D., Ziegler, Andreas · 2014 · Biometrical Journal
58. Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in Bioinformatics Applications
    Pengyi Yang, Yoo, Paul D., Fernando, Juanita, Zhou, Bing B., Zili Zhang, Zomaya, Albert Y. · 2014 · IEEE Transactions on Cybernetics
59. Selecting Representative Data Sets
    Borovicka, Tomas, Jirina, Marcel, Kordik, Pavel, Jiri, Marcel · 2012 · Advances in Data Mining Knowledge Discovery and Applications
60. Detecting, Tracing, and Monitoring Architectural Tactics in Code
    Mirakhorli, Mehdi, Cleland-Huang, Jane · 2016 · IEEE Transactions on Software Engineering
61. CYCLoPs: A Comprehensive Database Constructed from Automated Analysis of Protein Abundance and Subcellular Localization Patterns in<i>Saccharomyces cerevisiae</i>
    Koh, Judice L Y, Chong, Yolanda T, Friesen, Helena, Moses, Alan, Boone, Charles, Andrews, Brenda J, Moffat, Jason · 2015 · G3 Genes|Genomes|Genetics
62. Decision tree and random forest implementations for fast filtering of sensor data
    Buschjager, Sebastian, Morik, Katharina · 2018 · IEEE Transactions on Circuits and Systems I Regular Papers
63. Surgery for paroxysmal atrial fibrillation in the setting of mitral valve disease: a role for pulmonary vein isolation?
    Gillinov, A. Marc, Bakaeen, Faisal, McCarthy, Patrick M., Blackstone, Eugene H., Rajeswaran, Jeevanantham, Pettersson, Gosta, Sabik, Joseph F., Najam, Farzad, Hill, Kathleen M., Svensson, Lars G. et al. · 2006 · The Annals of Thoracic Surgery
64. BioReader: a text mining tool for performing classification of biomedical literature
    Simon, Christian, Davidsen, Kristian, Hansen, Christina, Seymour, Emily, Barnkob, Mike Bogetofte, Olsen, Lars Rønn · 2019 · BMC Bioinformatics
65. Machine Learning-Based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-Of-Concept Study
    Grosu, Sergio, Wesp, Philipp, Graser, Anno, Maurus, Stefan, Schulz, Christian, Knösel, Thomas, Cyran, Clemens C., Ricke, Jens, Ingrisch, Michael, Kazmierczak, Philipp M. · 2021 · Radiology
66. Assessing the Capacity of Social Determinants of Health Data to Augment Predictive Models Identifying Patients in Need of Wraparound Social Services
    Kasthurirathne, Suranga N, Vest, Joshua R, Menachemi, Nir, Halverson, Paul K, Grannis, Shaun J · 2018 · Journal of the American Medical Informatics Association
67. Estimating residential energy consumption in metropolitan areas: A microsimulation approach
    Zhang, Wenwen, Robinson, Caleb, Guhathakurta, Subhrajit, Garikapati, Venu M., Dilkina, Bistra, Brown, Marilyn A., Pendyala, Ram M. · 2018 · Energy
68. Conserved rules govern genetic interaction degree across species
    Koch, Elizabeth N, Costanzo, Michael, Bellay, Jeremy, Deshpande, Raamesh, Chatfield-Reed, Kate, Chua, Gordon, D'Urso, Gennaro, Andrews, Brenda J, Boone, Charles, Myers, Chad L · 2012 · Genome Biology
69. A machine learning approach to improve congestion control over wireless computer networks
    Geurts, P., El Khayat, I., Leduc, G. · 2004 · Fourth IEEE International Conference on Data Mining (ICDM'04)
70. A machine learning approach to live migration modeling
    Jo, Changyeon, Cho, Youngsu, Egger, Bernhard · 2017 · Proceedings of the 2017 Symposium on Cloud Computing
71. Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech
    Krajewski, Jarek, Schnieder, Sebastian, Sommer, David, Batliner, Anton, Schuller, Björn · 2012 · Neurocomputing
72. Dynamic-based damage identification using neural network ensembles and damage index method
    Dackermann, Ulrike, Li, Jianchun, Samali, Bijan · 2010 · Advances in Structural Engineering
73. Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
    Shadman Roodposhti, Majid, Aryal, Jagannath, Lucieer, Arko, Bryan, Brett A. · 2019 · Entropy
74. Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier
    Li, Feng, Xia, Yi, Wang, Fei, Zhang, Dengyong, Li, Xiaoyu, He, Fan · 2020 · Applied Sciences
75. Ensemble Deep Learning for Biomedical Time Series Classification
    Jin, Lin-peng, Dong, Jun · 2016 · Computational Intelligence and Neuroscience
76. A Decision-Support Tool for Renal Mass Classification
    Kunapuli, Gautam, Varghese, Bino A., Ganapathy, Priya, Desai, Bhushan, Cen, Steven, Aron, Manju, Gill, Inderbir, Duddalwar, Vinay · 2018 · Journal of Digital Imaging
77. A review of machine learning prediction methods for anxiety disorders
    Pintelas, Emmanuel G., Kotsilieris, Theodore, Livieris, Ioannis E., Pintelas, Panagiotis · 2018 · Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
78. Comparing Churn Prediction Techniques and Assessing Their Performance: A Contingent Perspective
    Tamaddoni, Ali, Stakhovych, Stanislav, Ewing, Michael · 2016 · Journal of Service Research
79. Impact of nutritional state on lung transplant outcomes
    Chamogeorgakis, Themistokles, Mason, David P., Murthy, Sudish C., Thuita, Lucy, Raymond, Daniel P., Pettersson, Gösta B., Blackstone, Eugene H. · 2013 · The Journal of Heart and Lung Transplantation
80. Temporal feature aggregation with attention for insider threat detection from activity logs
    Pal, Preetam, Chattopadhyay, Pratik, Swarnkar, Mayank · 2023 · Expert Systems with Applications
81. An integrated approach of ensemble learning methods for stock index prediction using investor sentiments
    Deng, Shangkun, Zhu, Yingke, Yu, Yiting, Huang, Xiaoru · 2024 · Expert Systems with Applications
82. Machine-learning models for combinatorial catalyst discovery
    Landrum, Gregory A, Penzotti, Julie E, Putta, Santosh · 2005 · Measurement Science and Technology
83. Deep Big Multilayer Perceptrons for Digit Recognition
    Cireşan, Dan Claudiu, Meier, Ueli, Gambardella, Luca Maria, Schmidhuber, Jürgen · 2012 · Lecture Notes in Computer Science
84. Ensemble learning of rule-based evolutionary algorithm using multi-layer perceptron for supporting decisions in stock trading problems
    Mabu, Shingo, Obayashi, Masanao, Kuremoto, Takashi · 2015 · Applied Soft Computing
85. Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach
    Fraccaro, Paolo, Nicolo, Massimo, Bonetto, Monica, Giacomini, Mauro, Weller, Peter, Traverso, Carlo Enrico, Prosperi, Mattia, OSullivan, Dympna · 2015 · BMC Ophthalmology
86. Data-driven reverse engineering of signaling pathways using ensembles of dynamic models
    Henriques, David, Villaverde, Alejandro F., Rocha, Miguel, Saez-Rodriguez, Julio, Banga, Julio R. · 2017 · PLOS Computational Biology
87. A fast method of steel surface defect detection using decision trees applied to LBP based features
    Aghdam, Sina Rezaei, Amid, Ehsan, Imani, Mohammad Faghih · 2012 · 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)
88. Artificial intelligence in radiotherapy
    Li, Guangqi, Wu, Xin, Ma, Xuelei · 2022 · Seminars in Cancer Biology
89. On the effect of calibration in classifier combination
    Bella, Antonio, Ferri, Cèsar, Hernández-Orallo, José, Ramírez-Quintana, María José · 2013 · Applied Intelligence
90. Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study
    Fritz, Bradley A, Chen, Yixin, Murray-Torres, Teresa M, Gregory, Stephen, Ben Abdallah, Arbi, Kronzer, Alex, McKinnon, Sherry Lynn, Budelier, Thaddeus, Helsten, Daniel L, Wildes, Troy S et al. · 2018 · BMJ Open
91. Pretransplant gastroesophageal reflux compromises early outcomes after lung transplantation
    Murthy, Sudish C., Nowicki, Edward R., Mason, David P., Budev, Marie M., Nunez, Anthony I., Thuita, Lucy, Chapman, Jeffrey T., McCurry, Kenneth R., Pettersson, Gösta B., Blackstone, Eugene H. · 2011 · The Journal of Thoracic and Cardiovascular Surgery
92. Predicting employee absenteeism for cost effective interventions
    Lawrance, Natalie, Petrides, George, Guerry, Marie-Anne · 2021 · Decision Support Systems
93. A Q-learning-based multi-agent system for data classification
    Pourpanah, Farhad, Tan, Choo Jun, Lim, Chee Peng, Mohamad-Saleh, Junita · 2017 · Applied Soft Computing
94. Classification of BGP anomalies using decision trees and fuzzy rough sets
    Li, Yan, Xing, Hong-Jie, Hua, Qiang, Wang, Xi-Zhao, Batta, Prerna, Haeri, Soroush, Trajkovic, Ljiljana · 2014 · 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
95. Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model
    Larburu, Nekane, Artetxe, Arkaitz, Escolar, Vanessa, Lozano, Ainara, Kerexeta, Jon · 2018 · Mobile Information Systems
96. Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning
    El Khayat, Ibtissam, Geurts, Pierre, Leduc, Guy · 2010 · Wireless Networks
97. On to the next chapter for crop breeding: Convergence with data science
    Ersoz, Elhan S., Martin, Nicolas F., Stapleton, Ann E. · 2020 · Crop Science
98. Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach (Preprint)
    Rodrigo, Hansapani, Beukes, Eldré W, Andersson, Gerhard, Manchaiah, Vinaya · 2021
99. Linking surface hydrodynamics to planktonic ecosystem: the case study of the ichthyoplanktonic assemblages in the Central Mediterranean Sea
    Cuttitta, Angela, Torri, Marco, Zarrad, Rafik, Zgozi, Salem, Jarboui, Othman, Quinci, Enza Maria, Hamza, Mohamed, Abdulfatah, Elfetori, Haddoud, Daw, El Turki, Akram et al. · 2018 · Hydrobiologia
100. Sample Subset Optimization for Classifying Imbalanced Biological Data
     Yang, Pengyi, Zhang, Zili, Zhou, Bing B., Zomaya, Albert Y. · 2011 · Lecture Notes in Computer Science