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Feature Selection Boosted by Unselected Features
IEEE Transactions on Neural Networks and Learning Systems, 2022Feature 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 ...
Shuo Chen, Fa Zhu, Hui Yan
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Learning With Selected Features
IEEE Transactions on Cybernetics, 2022The 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 +2 more
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Feature Selection for Classification
Intelligent Data Analysis, 1997Feature 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.
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Parallelizing Feature Selection
Algorithmica, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jerffeson Teixeira de Souza +2 more
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2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015
This work introduces adversarial feature selection, a game between a feature selection agent and its adversary. The adversarial approach is drawn from existing work on adversarial classification. The feature selection algorithm selects a subset of features from the original set based on their utility towards classification accuracy.
Karan Kumar Budhraja, Tim Oates 0001
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This work introduces adversarial feature selection, a game between a feature selection agent and its adversary. The adversarial approach is drawn from existing work on adversarial classification. The feature selection algorithm selects a subset of features from the original set based on their utility towards classification accuracy.
Karan Kumar Budhraja, Tim Oates 0001
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Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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IEEE Intelligent Systems, 2005
Data preprocessing is an indispensable step in effective data analysis. It prepares data for data mining and machine learning, which aim to turn data into business intelligence or knowledge. Feature selection is a preprocessing technique commonly used on high-dimensional data.
Huan Liu 0001 +12 more
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Data preprocessing is an indispensable step in effective data analysis. It prepares data for data mining and machine learning, which aim to turn data into business intelligence or knowledge. Feature selection is a preprocessing technique commonly used on high-dimensional data.
Huan Liu 0001 +12 more
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ON AUTOMATIC FEATURE SELECTION
International Journal of Pattern Recognition and Artificial Intelligence, 1988We review recent research on methods for selecting features for multidimensional pattern classification. These methods include nonmonotonicity-tolerant branch-and-bound search and beam search. We describe the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms.
Wojciech W. Siedlecki, Jack Sklansky
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Nonparametric feature selection
IEEE Transactions on Information Theory, 1969Two groups of L -dimensional observations of size N_{1} and N_{2} are known to be random vector variables from two unknown probability distribution functions [1]. A method is discussed for obtaining an l -dimensional linear subspace of the observation space in which the l -variate marginal distributions are most separated, based on a nonparametric ...
Edward A. Patrick +1 more
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