Results 11 to 20 of about 2,877,717 (359)
Dual-Regularized Feature Selection for Class-Specific and Global Feature Associations [PDF]
EntropyUnderstanding 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]
ACM 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]
Journal 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]
, 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]
PLoS ONETraditional 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]
, 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
Mathematics, 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
Applied 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]
Computer 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]
IEEE 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