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
Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features
Daniel Chong,Hyun J. Kim,Pechin Lo,S Young,Michael F. McNitt-Gray,Fereidoun Abtin,Jonathan G. Goldin,Matthew S. Brown +7 more
TL;DR: RDFS is highly effective at improving classifier robustness against slice thickness, reconstruction kernel, and tube current without sacrificing performance, a result that has implications for multicenter clinical trials that rely on accurate and reproducible quantitative analysis of CT images collected under varied conditions across multiple sites, scanners, and timepoints.
Journal Article
A comparative evaluation of medium-and large-scale feature selectors for pattern classifiers
Mineichi Kudo,Jack Sklansky +1 more
TL;DR: This study proposes a unified way to compare a large variety of algorithms and shows that the sequential floating algorithms promises for up to medium problems and genetic algorithms for medium and large problems.
Proceedings ArticleDOI
A wearable triaxial accelerometry system for longitudinal assessment of falls risk
Michael R. Narayanan,Maria Elena Scalzi,Stephen J. Redmond,Steven R. Lord,Branko G. Celler,Nigel H. Lovell +5 more
TL;DR: A waist-mounted triaxial accelerometry (Triax) system with a remote data collection capability to provide unsupervised monitoring of the elderly and an initial evaluation of the DR results to detect early changes in functional ability and facilitate falls risk stratification is presented.
Journal ArticleDOI
Feature selection toolbox
Petr Somol,Pavel Pudil +1 more
TL;DR: A software package developed for the purpose of feature selection in statistical pattern recognition is presented, which includes both several classical and new methods suitable for dimensionality reduction, classification and data representation.
Journal ArticleDOI
A novel combinatorial optimization based feature selection method for network intrusion detection
Anjum Nazir,Rizwan Ahmad Khan +1 more
TL;DR: In this paper, a wrapper-based feature selection method called "Tabu Search - Random Forest (TS-RF)" was proposed for Network Intrusion Detection Systems (NIDS) to reduce dimensionality of data.
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.