Results 321 to 330 of about 6,871,211 (381)

Learning With Selected Features

IEEE Transactions on Cybernetics, 2022
The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innovation, due to its computational and storage bottlenecks.
Shao-Bo Lin, Jian Fang, Xiangyu Chang
openaire   +3 more sources

Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

IEEE transactions on fuzzy systems, 2021
For heterogeneous data sets containing numerical and symbolic feature values, feature selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very significant step to preprocess data and improve its classification performance. This
Lin Sun, Lanying Wang, Weiping Ding, Y. Qian, Jiucheng Xu   +4 more
semanticscholar   +1 more source

Feature Selection Boosted by Unselected Features

IEEE Transactions on Neural Networks and Learning Systems, 2022
Feature selection aims to select strongly relevant features and discard the rest. Recently, embedded feature selection methods, which incorporate feature weights learning into the training process of a classifier, have attracted much attention. However, traditional embedded methods merely focus on the combinatorial optimality of all selected features ...
Wei Zheng, Shuo Chen, Zhenyong Fu, Fa Zhu, Hui Yan, Jian Yang   +5 more
openaire   +3 more sources

A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021
Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data.
Di Wu, Yi He, Xin Luo, Mengchu Zhou
semanticscholar   +1 more source

Feature Interaction for Streaming Feature Selection

IEEE Transactions on Neural Networks and Learning Systems, 2021
Traditional feature selection methods assume that all data instances and features are known before learning. However, it is not the case in many real-world applications that we are more likely faced with data streams or feature streams or both. Feature streams are defined as features that flow in one by one over time, whereas the number of training ...
Peng Zhou, Peipei Li, Shu Zhao, Xindong Wu   +3 more
openaire   +2 more sources

Infinite Feature Selection

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of ...
ROFFO, GIORGIO, MELZI, SIMONE, CRISTANI, Marco   +2 more
openaire   +3 more sources

Feature Selection

2009
Many scientific disciplines use modelling and simulation processes and techniques in order to implement non-linear mapping between the input and the output variables for a given system under study. Any variable that helps to solve the problem may be considered as input.
Noelia Sánchez-Maroño, Amparo Alonso-Betanzos   +1 more
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Feature Selection

2014
In this chapter we consider the implementation of feature selection approaches within the Conformal Predictor framework. We begin with a review of feature selection, then consider several approaches to implementation. First, we use existing feature selection methods within conformal predictors, which raises some computational issues.
Rama Chellappa, Pavan Turaga
openaire   +3 more sources

Feature Selection

2015
This chapter introduces a preprocessing step that is critical for a successful predictive modeling exercise: feature selection. Feature selection is known by several alternative terms such as attribute weighting, dimension reduction, and so on. There are two main styles of feature selection: filtering the key attributes before modeling (filter style ...
Vijay Kotu, Bala Deshpande
openaire   +2 more sources

From explanations to feature selection: assessing SHAP values as feature selection mechanism

SIBGRAPI Conference on Graphics, Patterns and Images, 2020
Explainability has become one of the most discussed topics in machine learning research in recent years, and although a lot of methodologies that try to provide explanations to black-box models have been proposed to address such an issue, little ...
W. E. Marcilio, D. M. Eler
semanticscholar   +1 more source