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
Semantic role labeling of implicit arguments for nominal predicates
Matthew S. Gerber,Joyce Y. Chai +1 more
TL;DR: Using a corpus of implicit arguments for ten predicates from NomBank, a discriminative model is trained that is able to identify implicit arguments with an F1 score of 50%, significantly outperforming an informed baseline model.
Posted Content
Multi-Atlas Segmentation of Biomedical Images: A Survey
TL;DR: A survey of published MAS algorithms and studies that have applied these methods to various biomedical problems and a perspective on the future of MAS, which, it is believed, will be one of the dominant approaches in biomedical image segmentation.
Journal ArticleDOI
Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals
TL;DR: Applying the forehead or physiological signals in the proposed scheme indicates that designing a reliable emotion recognition system is feasible without the need for additional emotional modalities.
Journal ArticleDOI
Selection of relevant features for EEG signal classification of schizophrenic patients
TL;DR: In this paper, a two-stage feature selection algorithm is designed, such that, the more informative channels are first selected to enhance the discriminative information, and then genetic algorithm (GA) is employed to select the best features from the selected channels.
Journal ArticleDOI
Feature selection and blind source separation in an EEG-based brain-computer interface
TL;DR: It is hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct "yes"/"no" BCI from a single session and the results suggest that BSS and feature selection can be use to improve the performance of even a "direct," single-session BCI.
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.
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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.