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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Proceedings ArticleDOI
AdaBoost with totally corrective updates for fast face detection
Jan Sochman,J. Malas +1 more
TL;DR: An extension of the AdaBoost learning algorithm is proposed and brought to bear on the face detection problem, and the correction steps are proven to lower the upper bound on the error without increasing computational complexity of the resulting detector.
Bayesian machine learning applied in a brain-computer interface for disabled users
TL;DR: The goal of this thesis is to extend the functionality of pattern recognition algorithms for BCI systems and to move towards systems that are helpful for disabled users by discussing extensions of linear discriminant analysis (LDA), which is a simple but efficient method for pattern recognition.
Journal ArticleDOI
Phased searching with NEAT in a Time-Scaled Framework: Experiments on a computer-aided detection system for lung nodules
TL;DR: A novel approach that combines feature selection with the evolution of ANN topology and weights is presented, compared with the original threshold-based Phased Searching method of Green, that requires fewer parameters and converges to the optimal network complexity required for the classification task at hand.
Journal ArticleDOI
A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data
TL;DR: A new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system that improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods.
Journal ArticleDOI
Acoustic feature selection and classification of emotions in speech using a 3D continuous emotion model
TL;DR: The feasibility of applying the continuous emotion models approach to annotation of emotional speech is demonstrated and ways to take advantage of this kind of annotation to improve the automatic classification of basic emotions are explored.
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.