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
Stress and anxiety detection using facial cues from videos
Giorgos Giannakakis,Matthew Pediaditis,Dimitris Manousos,Eleni Kazantzaki,Franco Chiarugi,Panagiotis G. Simos,Panagiotis G. Simos,Kostas Marias,Manolis Tsiknakis,Manolis Tsiknakis +9 more
TL;DR: Specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.
Remote sensing techniques for mangrove mapping
TL;DR: It was found that appropriate data treatments and analysis techniques were still required to harness essential information from both hyperspectral and ecological data, so it is hoped that the methodology presented will prove useful and will be followed for producing mangrove maps at a finer level.
Journal ArticleDOI
Training cellular automata for image processing
TL;DR: The sequential floating forward search method for feature selection was used to select good rule sets for a range of tasks, namely noise filtering, noise filtering using threshold decomposition, thinning, and convex hulls.
Journal ArticleDOI
SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier
TL;DR: The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.
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Random subspace method for multivariate feature selection
TL;DR: A new multivariate search technique is introduced, that is less sensitive to the noise in the data and computationally feasible as well and the robustness and reliability of the novel multivariate feature selection method are compared.
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.