Results 81 to 90 of about 3,398,718 (314)
Predicting IPO initial returns using random forest
Borsa Istanbul Review, 2020 Empirical analyses of IPO initial returns are heavily dependent on linear regression models. However, these models can be inefficient due to its sensitivity to outliers which are common in IPO data.
Boubekeur Baba, Güven Sevil
doaj +1 more source
Advances in Neural Information Processing Systems 38We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally represents relationships in the data. We provide exact and approximate solutions to the decoding problem via constrained
Binh Duc Vu +3 more
openaire +2 more sources
CoRR, 2020 Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nature, however, they lack principled methods to process inputs with missing features or to detect outliers, which requires pairing them with ...
Alvaro H. C. Correia +2 more
openaire +4 more sources
Framework of Random Forest algorithm.
, 2021 Framework of Random Forest algorithm.
Jun Xu (45543) +2 more
core +1 more source
Accurate and noninvasive prostate cancer detection using plasma‐derived extracellular vesicle RNA
FEBS Open Bio, EarlyView.Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Hanping Wei, Haoran Wu, Wei Feng
wiley +1 more source
Bioinformatics, 2008 Abstract Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly.
Dhammika Amaratunga +2 more
openaire +3 more sources
A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting [PDF]
, 2014 This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier.
Tarun Rao
core
FEBS Open Bio, EarlyView.Using peripheral blood for determining B‐cell or T‐cell clonality is more reliable when we use cell‐free RNA (cfRNA) because cells release blood significantly more RNA than DNA. Next‐generation sequencing (NGS) of cfRNA allows us to evaluate fragment cfRNA and evaluate clonality reliably without the need for prior determination of the specific dominant
Adam Albitar +11 more
wiley +1 more source
Meta random forest : random forest with simple random forest as base model
, 2020 The abundance of data along with the significant rise of computational power boost the popularity and usage of machine learning in recent decision making and forecasting.
Kurniawan, Billy
core
Aging and Cancer, EarlyView.Cancer treatment is associated with measurable acceleration of biological aging across epigenetic, telomere, senescence, and immune biomarkers. However, biomarker validation and interventional strategies remain limited, especially in hematologic malignancies, underscoring the need for standardized multi‐omic aging assessments and adequately powered ...
Moataz Ellithi +3 more
wiley +1 more source