Journal ArticleDOI
Floating search methods in feature selection
TLDR
Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented and are shown to give very good results and to be computationally more effective than the branch and bound method.About:
This article is published in Pattern Recognition Letters.The article was published on 1994-11-01. It has received 3104 citations till now. The article focuses on the topics: Beam search & Jump search.read more
Citations
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Journal ArticleDOI
Speckle reduction for the forest mapping analysis of multi-temporal Radarsat-1 images
TL;DR: Spatial speckle filters offer the advantage of higher radiometric resolution and higher classification accuracy with lower algorithm complexity than MT filters, confirming that MT filtering offers no advantage for classification applications.
Journal ArticleDOI
Band-Specified Virtual Dimensionality for Band Selection: An Orthogonal Subspace Projection Approach
TL;DR: A new Neyman–Pearson detection approach to estimating the number of bands required by band selection (BS), as well as finding desired bands at the same time, using NPD to determine and rank desired bands for band prioritization.
Journal ArticleDOI
Digital forensics of printed source identification for Chinese characters
TL;DR: The superior testing performance demonstrates that the proposed identification method is very useful for source laser printer identification and explores the optimum feature subset by using feature selection techniques and use support vector machine (SVM) to identify the source model of the documents.
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Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection
TL;DR: This paper proposes a novel hybrid filter-wrapper algorithm, called Ensemble of Filter-based Rankers to guide an Epsilon-greedy Swarm Optimizer (EFR-ESO), for solving high-dimensional feature subset selection.
Journal ArticleDOI
Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C
Zheng Jiang,Kazunobu Yamauchi,Kentaro Yoshioka,Aoki Kazuma,Susumu Kuroyanagi,Akira Iwata,Jun Yang,Kai Wang +7 more
TL;DR: This study showed that application of this model could identify CHC patients with clinically significant fibrosis with a high degree of accuracy and may decrease the need for liver biopsy.
References
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Journal ArticleDOI
A Branch and Bound Algorithm for Feature Subset Selection
TL;DR: In this paper, a branch and bound-based feature subset selection algorithm is proposed to select the best subset of m features from an n-feature set without exhaustive search, which is computationally computationally unfeasible.
Journal ArticleDOI
A note on genetic algorithms for large-scale feature selection
W. Siedlecki,Jack Sklansky +1 more
TL;DR: The preliminary results suggest that GA is a powerful means of reducing the time for finding near-optimal subsets of features from large sets.
Journal ArticleDOI
A Direct Method of Nonparametric Measurement Selection
TL;DR: A direct method of measurement selection is proposed to determine the best subset of d measurements out of a set of D total measurements, using a nonparametric estimate of the probability of error given a finite design sample set.
Journal ArticleDOI
On the effectiveness of receptors in recognition systems
T. Marill,D. Green +1 more
TL;DR: Some of the theoretical problems encountered in trying to determine a more formal measure of the effectiveness of a set of tests are discussed; a measure which might be a practical substitute for the empirical evaluation.
Journal ArticleDOI
On automatic feature selection
W. Siedlecki,Jack Sklansky +1 more
TL;DR: In this paper, a review of feature selection for multidimensional pattern classification is presented, and the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms are compared.