Results 241 to 250 of about 717,161 (269)

Feature condensing algorithm for feature selection

2008 19th International Conference on Pattern Recognition, 2008
A new unsupervised filter-based feature selection method is introduced. Its principle consists in merging similar features into clusters using a distance measure derived from the correlation coefficient. Subsequently, only one representative feature is selected from each cluster.
Pavel Krízek, Josef Kittler, Václav Hlavác   +2 more
openaire   +1 more source

Group Feature Selection with Streaming Features

2013 IEEE 13th International Conference on Data Mining, 2013
Group feature selection makes use of structural information among features to discover a meaningful subset of features. Existing group feature selection algorithms only deal with pre-given candidate feature sets and they are incapable of handling streaming features.
Hai-Guang Li, Xindong Wu 0001, Zhao Li 0007, Wei Ding 0003   +3 more
openaire   +1 more source

Streamwise feature selection

J. Mach. Learn. Res., 2006
Summary: In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature selection offers many advantages over traditional feature selection methods, which assume that all features are known in advance.
Zhou, Jing, Stine, Robert A, Foster, Dean P, Ungar, Lyle H.   +3 more
openaire   +3 more sources

Ensemble Feature Selection for Rankings of Features

2015
In the last few years, ensemble learning has been the focus of much attention mainly in classification tasks, based on the assumption that combining the output of multiple experts is better than the output of any single expert. This idea of ensemble learning can be adapted for feature selection, in which different feature selection algorithms act as ...
Borja Seijo-Pardo, Verónica Bolón-Canedo, Iago Porto-Díaz, Amparo Alonso-Betanzos   +3 more
openaire   +1 more source

Combining Feature Subsets in Feature Selection

2005
In feature selection, a part of the features is chosen as a new feature subset, while the rest of the features is ignored. The neglected features still, however, may contain useful information for discriminating the data classes. To make use of this information, the combined classifier approach can be used.
Marina Skurichina, Robert P. W. Duin
openaire   +1 more source

Causality-based Feature Selection

ACM Computing Surveys, 2021
Xianjie Guo, Lin Liu, Jiuyong Li
exaly  

Feature Grouping-based Feature Selection

2017
Feature selection (FS) is a process which aims to select input domain features that are most informative for a given outcome. Unlike other dimensionality reduction techniques, feature selection methods preserve the underlying semantics or meaning of the original data following reduction.
openaire   +2 more sources

A comprehensive survey on recent metaheuristics for feature selection

Neurocomputing, 2022
Håkan Ezgi Kiziloz, Ayça Deniz, Tansel Dokeroglu   +2 more
exaly  

Incremental feature selection by sample selection and feature-based accelerator

Applied Soft Computing, 2022
Yanyan Yang 0001, Degang Chen 0002, Xiao Zhang 0012, Zhenyan Ji, Yingjun Zhang   +4 more
openaire   +1 more source

Feature Selection

ACM Computing Surveys, 2018
Jundong Li, Huan Liu, Suhang Wang
exaly