Results 11 to 20 of about 2,877,717 (359)

Dual-Regularized Feature Selection for Class-Specific and Global Feature Associations [PDF]

open access: yesEntropy
Understanding feature associations is vital for selecting the most informative features. Existing methods primarily focus on global feature associations, which capture overall relationships across all samples.
Chenchen Wang   +4 more
doaj   +2 more sources

Feature Selection: A Data Perspective [PDF]

open access: yesACM Computing Surveys, 2017
Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems.
Cheng, Kewei   +6 more
core   +4 more sources

Feature Selection for Functional Data [PDF]

open access: yesJournal of Multivariate Analysis, 2015
In this paper we address the problem of feature selection when the data is functional, we study several statistical procedures including classification, regression and principal components.
Fraiman, Ricardo   +2 more
core   +5 more sources

Digging into acceptor splice site prediction : an iterative feature selection approach [PDF]

open access: bronze, 2004
Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data.
A.I. Blum   +18 more
core   +4 more sources

Unsupervised feature selection algorithm based on L 2,p-norm feature reconstruction. [PDF]

open access: yesPLoS ONE
Traditional subspace feature selection methods typically rely on a fixed distance to compute residuals between the original and feature reconstruction spaces.
Wei Liu   +5 more
doaj   +2 more sources

Agnostic Feature Selection [PDF]

open access: yes, 2020
Unsupervised feature selection is mostly assessed along a supervised learning setting, depending on whether the selected features efficiently permit to predict the (unknown) target variable. Another setting is proposed in this paper: the selected features aim to efficiently recover the whole dataset.
Doquet, Guillaume Florent   +1 more
openaire   +5 more sources

Redundancy Is Not Necessarily Detrimental in Classification Problems

open access: yesMathematics, 2021
In feature selection, redundancy is one of the major concerns since the removal of redundancy in data is connected with dimensionality reduction. Despite the evidence of such a connection, few works present theoretical studies regarding redundancy.
Sebastián Alberto Grillo   +9 more
doaj   +1 more source

Hybrid-Recursive Feature Elimination for Efficient Feature Selection

open access: yesApplied Sciences, 2020
As datasets continue to increase in size, it is important to select the optimal feature subset from the original dataset to obtain the best performance in machine learning tasks.
Hyelynn Jeon, Sejong Oh
doaj   +1 more source

Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index [PDF]

open access: yesComputer Science Journal of Moldova, 2022
Feature selection is vital in data pre-processing in machine learning, and it is prominent in datasets with many features. Feature selection analyses the relevant, irrelevant, and redundant features in the dataset.
Usharani Bhimavarapu, M. Sreedevi
doaj   +1 more source

Online Feature Selection with Streaming Features [PDF]

open access: yesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed.
Xindong Wu   +4 more
openaire   +3 more sources

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