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
Simultaneous feature selection and Gaussian mixture model estimation for supervised classification problems
TL;DR: The main findings are the following: Feature selection is very important in terms of prediction quality of models and the proposed method estimates better models than other state-of-the-art methods.
Journal ArticleDOI
Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data
TL;DR: This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification, and a comparison is made with four state-of-the-art related algorithms.
Proceedings ArticleDOI
Choosing an Optimal Neural Network Size to Aid a Search through a Large Image Database
Kieron Messer,Josef Kittler +1 more
TL;DR: The proposed method of selecting both input features and hidden neurons avoids the pitfalls exhibited by other methods reported in the literature and the resulting network architecture is extremely lean while at the same time significantly improving the network performance.
Journal ArticleDOI
Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions
TL;DR: The results show that in realistic driving conditions, subject-independent sleepiness classification based on HRV is poor, and more work is needed to control for the many confounding factors that also influence HRV before it can be used as input to a driver sleepiness detection system.
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.