Results 61 to 70 of about 404,815 (264)

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   +1 more source

Autoencoding Random Forests

CoRR
We 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, Jan Kapar, Marvin N. Wright, David S. Watson   +3 more
openaire   +2 more sources

Boosted Random Forest

IEICE Transactions on Information and Systems, 2015
Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest
MISHINA, Yohei, MURATA, Ryuei, YAMAUCHI, Yuji, YAMASHITA, Takayoshi, FUJIYOSHI, Hironobu   +4 more
openaire   +2 more sources

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig, Nadav Amir, Maya Schiller, Roni Loebenstein, Johnatan Nissan, Marina Boxer, Shany Guly Gofrit, Gal Goshen, Sándor Beniczky, Nicola Maggio   +9 more
wiley   +1 more source

Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang, Dongxu Zhang, Dongyuan Liu, Da Lin, Hao Wang   +4 more
wiley   +1 more source

Subtractive random forests

Latin American Journal of Probability and Mathematical Statistics
Motivated by online recommendation systems, we study a family of random forests. The vertices of the forest are labeled by integers. Each non-positive integer $i\le 0$ is the root of a tree. Vertices labeled by positive integers $n \ge 1$ are attached sequentially such that the parent of vertex $n$ is $n-Z_n$, where the $Z_n$ are i.i.d.\ random ...
Broutin, Nicolas, Devroye, Luc, Lugosi, Gábor, Oliveira, Roberto Imbuzeiro   +3 more
openaire   +3 more sources

The Macroeconomy as a Random Forest [PDF]

SSRN Electronic Journal, 2020
SummaryI develop the macroeconomic random forest (MRF), an algorithm adapting the canonical machine learning (ML) tool, to flexibly model evolving parameters in a linear macro equation. Its main output, generalized time‐varying parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, and ...
openaire   +2 more sources

A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi, Giorgio Ricciardiello Mejia, Andre Metzger, Kang Hyun Ryu, Salonee Marwaha, Eva Grzegorczyk, Li Zhou, Eliana Jacobs, Boris Gilyadov, Claudia Kunney, Liqhwa Ncube, Ankit Parekh, Emmanuel Mignot, Fanny M. Elahi, Joseph Winer, Kathleen Poston, Andreas Brink‐Kjaer, Emmanuel H. During   +17 more
wiley   +1 more source

Random Forests for Multiclass Classification: Random [PDF]

, 2008
—Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain ...
Multinomial Logit, Random Multinomial Logit   +1 more
core  

Meta random forest : random forest with simple random forest as base model [PDF]

, 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