Results 281 to 290 of about 2,759,833 (308)

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   +2 more sources

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

Feature Selection

2023
Feature selection, also known as variable or descriptor selection, is the process of finding a subset of features to use with a given task and learner. Finding the optimal set of features can improve predictive performance, reduce noise in data, and make models easier to interpret.
Frederic Ros, Rabia Riad
  +6 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.
Robert Nisbet, John Elder, Gary Miner
  +5 more sources

Feature Selection for Classification

Intelligent Data Analysis, 1997
Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand.
Dash, M., Liu, H.
openaire   +1 more source

Parallelizing Feature Selection

Algorithmica, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
de Souza, Jerffeson Teixeira, Matwin, Stan, Japkowicz, Nathalie   +2 more
openaire   +2 more sources

Feature Selection and Feature Engineering

2019
Feature selection and engineering are important steps in a machine learning pipeline and involves all the techniques adopted to reduce their dimensionality. Most of the time, these steps come after cleaning the dataset.
Hisham El-Amir, Mahmoud Hamdy
openaire   +1 more source

Streamwise feature selection

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   +2 more sources

Feature Selection

2014
Salvador García, Julián Luengo, Francisco Herrera   +2 more
  +5 more sources

Feature Selection

Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence, 2021
Seema Chaudhary, Sangeeta Kakarwal, Chitra Gaikwad   +2 more
openaire   +1 more source