ArticleBioData mining2014
Synthetic learning machines.
Article in BioData mining, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Evaluation of geological hazards susceptibility along a key railway based on machine learning.Scientific reports · 2025Article
- Chorionic Gonadotropin Beta 7 is a marker of immune evasion in cancer.bioRxiv : the preprint server for biology · 2025Article
- Sulfur-Fumigated Ginger Identification Method Based on Meta-Learning for Different Devices.Foods (Basel, Switzerland) · 2024Article
- Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms.Foods (Basel, Switzerland) · 2024Article
- Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique.Foods (Basel, Switzerland) · 2024Article
- Strategies of Managing Repeated Measures: Using Synthetic Random Forest to Predict HIV Viral Suppression Status Among Hospitalized Persons with HIV.AIDS and behavior · 2023Article
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- Estimating heterogeneous survival treatment effect in observational data using machine learning.Statistics in medicine · 2021Article
- Inflammatory Biomarkers Aid in Diagnosis of Dementia.Frontiers in aging neuroscience · 2021Article
- Biomarkers identify the Binswanger type of vascular cognitive impairment.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2019Article
- Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2018Article
- The optimal crowd learning machine.BioData mining · 2017Article
- Validation of Claims Algorithms for Progression to Metastatic Cancer in Patients with Breast, Non-small Cell Lung, and Colorectal Cancer.Frontiers in oncology · 2016Article
- Optimizing pediatric "Mild" traumatic brain injury assessments: A multi-domain random forest analysis of diagnosis and outcomes.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundUsing a collection of different terminal nodesize constructed random forests, each generating a synthetic feature, a synthetic random forest is defined as a kind of hyperforest, calculated using the new input synthetic features, along with the original features.
resultsUsing a large collection of regression and multiclass datasets we show that synthetic random forests outperforms both conventional random forests and the optimized forest from the regresssion portfolio.
conclusionsSynthetic forests removes the need for tuning random forests with no additional effort on the part of the researcher. Importantly, the synthetic forest does this with evidently no loss in prediction compared to a well-optimized single random forest.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.