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
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge
TL;DR: The basic phenomenon reflecting the last fifteen years is addressed, commenting on databases, modelling and annotation, the unit of analysis and prototypicality and automatic processing including discussions on features, classification, robustness, evaluation, and implementation and system integration.
Proceedings ArticleDOI
Feature selection algorithms: a survey and experimental evaluation
TL;DR: This work assesses the performance of several fundamental algorithms found in the literature in a controlled scenario by taking into account the amount of relevance, irrelevance and redundance on sample data sets and a scoring measure ranks the algorithms.
Journal ArticleDOI
Simultaneous feature selection and clustering using mixture models
TL;DR: This paper proposes the concept of feature saliency and introduces an expectation-maximization algorithm to estimate it, in the context of mixture-based clustering, and extends the criterion and algorithm to simultaneously estimate the feature saliencies and the number of clusters.
Journal ArticleDOI
ECG-based heartbeat classification for arrhythmia detection
TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.
Journal ArticleDOI
Statistical texture characterization from discrete wavelet representations
TL;DR: It is conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore two feature sets are introduced: the wavelets histogram signatures which capture all first order statistics using a model based approach and the co-occurrence signatures which reflect the coefficients' second-order statistics.
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.