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
Consensus unsupervised feature ranking from multiple views
TL;DR: The proposed FRMV method firstly obtains multiple rankings of all features from different views of the same data set and then aggregates all the obtained feature rankings into a single consensus one.
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Integration of the saliency-based seed extraction and random walks for image segmentation
TL;DR: A novel automatic image segmentation method is proposed that combines the region saliency based on entropy rate superpixel (RSBERS) with the affinity propagation clustering algorithm to get seeds in an unsupervised manner, and uses random walks method to obtain the segmentation results.
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An evidential classifier based on feature selection and two-step classification strategy
TL;DR: A supervised learning method composed of a feature selection procedure and a two-step classification strategy that improves the decision-making accuracy and the performance of the proposed method was evaluated on various synthetic and real datasets.
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Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data
TL;DR: The results from this study demonstrate that the iterative BMA procedure selects a small number of genes while eclipsing other methods in predictive performance, making it a highly accurate and cost-effective prognostic tool in the clinical setting.
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Nearest-Neighbor Guided Evaluation of Data Reliability and Its Applications
Tossapon Boongoen,Qiang Shen +1 more
TL;DR: A more efficient nearest-neighbor-based reliability assessment for which an expensive clustering process is not required and which can be perceived as a stress function, from which the OWA weights and associated decision-support explanations can be generated.
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.