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91 |
Optimizing credit card fraud detection with random forests and SMOTE
Enthalten in Scientific reports Bd. 15, 22.5.2025, Nr. 1, date:12.2025: 1-12
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92 |
Percolation transition for random forests in d ⩾ 3 $d\geqslant 3$
Enthalten in Inventiones mathematicae Bd. 237, 15.5.2024, Nr. 2, date:8.2024: 445-540
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93 |
Performance of random forests and logic regression methods using mini-exome sequence data
Enthalten in Biomed Central (London): BMC proceedings Bd. 5, 29.11.2011, Nr. 9, date:12.2011: 1-6
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94 |
Phytoextraction capability of Azolla pinnata in the removal of rhodamine B from aqueous solution: artificial neural network and random forests approaches
Enthalten in Applied water science Bd. 9, 3.5.2019, Nr. 4, date:6.2019: 1-9
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95 |
Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data
Enthalten in Dermatology and therapy Bd. 12, 27.10.2022, Nr. 12, date:12.2022: 2747-2763
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96 |
Predicting postoperative surgical site infection with administrative data: a random forests algorithm
Enthalten in BMC medical research methodology Bd. 21, 28.8.2021, Nr. 1, date:12.2021: 1-11
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97 |
Predicting protein residue-residue contacts using random forests and deep networks
Enthalten in BMC bioinformatics Bd. 20, 14.3.2019, Nr. 2, date:3.2019: 115-127
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98 |
Predicting siRNA potency with random forests and support vector machines
Enthalten in BMC genomics Bd. 11, 01.12.2010, Nr. 3, date:12.2010: 1-7
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99 |
Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature
Enthalten in BMC bioinformatics Bd. 12, 17.8.2011, Nr. 1, date:12.2011: 1-10
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100 |
Prediction of DNA-binding residues from protein sequence information using random forests
Enthalten in BMC genomics Bd. 10, 7.7.2009, Nr. 1, date:7.2009: 1-9
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