Proceedings Article10.1145/1102351.1102383
Online feature selection for pixel classification
Karen A. Glocer,Damian Eads,James Theiler +2 more
- 07 Aug 2005
- pp 249-256
TL;DR: This paper applies online feature selection to the problem of edge detection in grayscale imagery and compares several different OFS approaches, including hill climbing, best first search, and grafting.
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Abstract: Online feature selection (OFS) provides an efficient way to sort through a large space of features, particularly in a scenario where the feature space is large and features take a significant amount of memory to store. Image processing operators, and especially combinations of image processing operators, provide a rich space of potential features for use in machine learning for image processing tasks but they are expensive to generate and store. In this paper we apply OFS to the problem of edge detection in grayscale imagery. We use a standard data set and compare our results to those obtained with traditional edge detectors, as well as with results obtained more recently using "statistical edge detection." We compare several different OFS approaches, including hill climbing, best first search, and grafting.
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Citations
Feature Selection: An Ever Evolving Frontier in Data Mining
Huan Liu,Hiroshi Motoda,Rudy Setiono,Zheng Zhao +3 more
- 26 May 2010
TL;DR: The key components of feature selection are introduced, and its developments with the growth of data mining are reviewed, and some potential lines of research that require multidisciplinary research are identified.
Recent advances and emerging challenges of feature selection in the context of big data
TL;DR: The origins and importance of feature selection are discussed and recent contributions in a range of applications are outlined, from DNA microarray analysis to face recognition.
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Online Feature Selection and Its Applications
TL;DR: This article investigates the problem of online feature selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features, and presents novel algorithms to solve each of the two problems.
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Online Feature Selection with Streaming Features
TL;DR: A novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly and an efficient Fast-OSFS algorithm is proposed to improve feature selection performance.
Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models
TL;DR: This work proposes a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself and achieves state-of-the-art performances even when using very small architectures trained from scratch.
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