Results 271 to 280 of about 796,511 (318)

ON AUTOMATIC FEATURE SELECTION

International Journal of Pattern Recognition and Artificial Intelligence, 1988
We 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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Feature selection toolbox

Pattern Recognition, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Petr Somol, Pavel Pudil
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Feature Selection and Feature Extraction: Highlights

Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2021
In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction.
Hiu-Man Wong, Xingjian Chen, Hiu-Hin Tam, Jiecong Lin, Shixiong Zhang 0002, Shankai Yan, Xiangtao Li, Ka-Chun Wong   +7 more
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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 0003, Zhenyong Fu, Fa Zhu, Hui Yan, Jian Yang 0003   +5 more
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Nonparametric feature selection

IEEE Transactions on Information Theory, 1969
Two 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, Frederic P. Fischer II   +1 more
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Curious Feature Selection

Information Sciences, 2019
Abstract In state-of-the-art big-data applications, the process of building machine learning models can be very challenging due to continuous changes in data structures and the need for human interaction to tune the variables and models over time. Hence, expedited learning in rapidly changing environments is required. In this work, we address this
Michal Moran, Goren Gordon
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Fuzzy feature selection

Pattern Recognition, 1999
In fuzzy classi"er systems the classi"cation is obtained by a number of fuzzy If}Then rules including linguistic terms such as Low and High that fuzzify each feature. This paper presents a method by which a reduced linguistic (fuzzy) set of a labeled multi-dimensional data set can be identi"ed automatically.
M. Ramze Rezaee, Bob Goedhart, Boudewijn P. F. Lelieveldt, Johan H. C. Reiber   +3 more
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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 0008, Peipei Li 0001, Shu Zhao 0005, Xindong Wu 0001   +3 more
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Transferred Feature Selection

2009 IEEE International Conference on Data Mining Workshops, 2009
Traditional feature selection algorithms require a large number of labeled training instances to find out the most informative subset of features. However, in many real-world applications, the labeled data are often difficult, expensive or time-consuming to obtain.
Wei Bi, Yuan Shi, Zhen-zhong Lan
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Structured Feature Selection

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Feature dimensionality reduction has been widely used in various computer vision tasks. We explore feature selection as the dimensionality reduction technique and propose to use a structured approach, based on the Markov Blanket (MB), to select features. We first introduce a new MB discovery algorithm, Simultaneous Markov Blanket (STMB) discovery, that
Tian Gao, Ziheng Wang 0001, Qiang Ji
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