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
Financial crisis prediction model using ant colony optimization
TL;DR: Experimental analysis shows that the ACO-FCP ensemble model is superior and more robust than its counterparts, and this study strongly recommends that the proposed ACO -FCP model is highly competitive than traditional and other artificial intelligence techniques.
Journal ArticleDOI
Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning
TL;DR: A novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space is proposed.
Journal ArticleDOI
A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets
TL;DR: This work proposes a stochastic algorithm based on the GRASP meta-heuristic, with the main goal of speeding up the feature subset selection process, basically by reducing the number of wrapper evaluations to carry out.
Journal ArticleDOI
An Automatic Patient-Adapted ECG Heartbeat Classifier Allowing Expert Assistance
TL;DR: The results presented in this paper represent an improvement in the field of automatic and patient-adaptable heartbeats classification, concluding that the performance of an automatic classifier can be improved with an efficient handling of the expert assistance.
Journal ArticleDOI
Prostate Cancer: Computer-aided Diagnosis with Multiparametric 3-T MR Imaging—Effect on Observer Performance
Thomas Hambrock,Pieter C. Vos,Christina A. Hulsbergen-van de Kaa,Jelle O. Barentsz,Henkjan J. Huisman +4 more
TL;DR: Addition of CAD significantly improved the performance of less-experienced observers in distinguishing benign from malignant lesions; when less- Experienced observers used CAD, they reached similar performance as experienced observers.
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.