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Simultaneous Feature Selection for Optimal Dynamic Treatment Regimens. [PDF]

Stat Med
Liu M, Wang Y, Zeng D.
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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
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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
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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
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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
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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
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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
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