Results 201 to 210 of about 28,526 (253)

Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study. [PDF]

BMC Med Inform Decis Mak
Emakhu J, Brooks RC, Adrianto I, Bossick AS, Haeusler J, Welchans M, Miller J, Levin AM, Su WK.   +8 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Incremental feature selection based on fuzzy rough sets

Information Sciences, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xizhao Wang, Suyun Zhao, Eric C C Tsang   +2 more
exaly   +2 more sources

Incremental feature selection with fuzzy rough sets for dynamic data sets

Fuzzy Sets and Systems, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Degang Chen, Lianjie Dong, Ruihong Wang   +2 more
exaly   +3 more sources

Incremental feature selection by sample selection and feature-based accelerator

Applied Soft Computing Journal, 2022
Xiao Zhang, Degang Chen, Zhenyan Ji
exaly   +2 more sources

Discernible neighborhood counting based incremental feature selection for heterogeneous data

International Journal of Machine Learning and Cybernetics, 2019
Incremental feature selection refreshes a subset of information-rich features from added-in samples without forgetting the previously learned knowledge. However, most existing algorithms for incremental feature selection have no explicit mechanisms to handle heterogeneous data with symbolic and real-valued features.
Degang Chen, Yanyan Yang, Song Shiji
exaly   +2 more sources

Online streaming feature selection with incremental feature grouping

WIREs Data Mining and Knowledge Discovery, 2020
AbstractToday, the dimensionality of data is increasing in a massive way. Thus, traditional feature selection techniques are not directly applicable. Consequently, recent research has led to the development of a more efficient approach to the selection of features from a feature stream, known as streaming feature selection. Another active research area,
Noura H. Al Nuaimi, Mohammad M. Masud 0001   +1 more
openaire   +1 more source

UNSUPERVISED FEATURE SELECTION USING INCREMENTAL LEAST SQUARES [PDF]

International Journal of Information Technology & Decision Making, 2011
An unsupervised feature selection method is proposed for analysis of datasets of high dimensionality. The least square error (LSE) of approximating the complete dataset via a reduced feature subset is proposed as the quality measure for feature selection. Guided by the minimization of the LSE, a kernel least squares forward selection algorithm (KLS-FS)
Rong Liu, Robert Rallo, Yoram Cohen
openaire   +1 more source