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
Nonlinear decision boundaries for testing analog circuits
TL;DR: A neural classifier that learns to separate the nominal from the faulty instances of a circuit in a measurement space is developed and is capable of drawing nonlinear hypersurfaces.
Journal ArticleDOI
Sensing and Classifying Roadway Obstacles in Smart Cities: The Street Bump System
TL;DR: An infrastructure-free approach for anomaly detection and identification based on data collected through a smartphone application (Street Bump) capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as prioritizing actionable ones in need of immediate attention based on a proposed anomaly index.
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Research issues in genomic signal processing
TL;DR: The authors discuss the key research issues for GSP and note that the research issues pertaining to GSP fit within the overall challenges confronting research in the area of multimodal biomedical systems.
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Automatic feature selection for context recognition in mobile devices
TL;DR: This study investigates how much advantage can be achieved by using sophisticated and complex classification methods compared with a simple method that can easily be implemented in mobile devices and reports superior performance for the Minimum-distance classifier from the view point of computational load and power consumption of a smart phone.
Journal ArticleDOI
Improved support vector classification using PCA and ICA feature space modification
Jeff Fortuna,David W. Capson +1 more
TL;DR: An approach that unifies subspace feature selection and optimal classification is presented and results are presented which provide lower classification error and better generalization than can be obtained by the SVC on the raw data and its PCA or ICA subspace representation.
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.
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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.
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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.