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Journal ArticleDOI

Floating search methods in feature selection

Pavel Pudil, +2 more
- 01 Nov 1994 - 
- Vol. 15, Iss: 11, pp 1119-1125
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.
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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.

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Citations
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Book ChapterDOI

Motor imagery and EEG-based control of spelling devices and neuroprostheses.

TL;DR: The basic methods used in Graz-BCI research are described and possible clinical applications are outlined, which can be used to assist patients who have highly compromised motor functions.
Journal ArticleDOI

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

TL;DR: The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats.
Journal ArticleDOI

Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits

TL;DR: It is shown that the use of automatic feature selection in paralinguistic analysis can be used to reduce the overall number of features to a fraction of the original feature set size while still achieving a comparable or even better performance than baseline support vector machine or random forest classifiers using the full feature set.
Book

Computational Methods

Journal ArticleDOI

A new wrapper feature selection approach using neural network

TL;DR: The aim of using correlation information in CAFS is to encourage the search strategy for selecting less correlated features if they enhance accuracy of NNs and reduce redundancy of information resulting in compact NN architectures.
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

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

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

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.
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