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
Functional Connectivity Analysis of Mental Fatigue Reveals Different Network Topological Alterations Between Driving and Vigilance Tasks
Georgios N. Dimitrakopoulos,Ioannis Kakkos,Zhongxiang Dai,Hongtao Wang,Kyriakos N. Sgarbas,Nitish V. Thakor,Anastasios Bezerianos,Yu Sun +7 more
TL;DR: The topological alterations of functional brain networks in the theta band of electroencephalography data from 40 male subjects undergoing two distinct fatigue-inducing tasks are investigated to demonstrate the feasibility of using functional connectivity as neural biomarkers for applicable fatigue monitoring.
Journal ArticleDOI
Classification of aquifer vulnerability using K-means cluster analysis
TL;DR: In this paper, a clustering technique is introduced that removes some of the subjectivity associated with the indexing method and creates a vulnerability map that does not rely on fixed weights and ratings and, thereby, provides a more objective representation of the system's physical characteristics.
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Texture feature coding method for classification of liver sonography
TL;DR: Experimental results show that the ML classifier together with TFCM texture features outperforms one with the four conventional methods with respect to classification accuracy.
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An automatic assessment scheme for steel quality inspection
TL;DR: A fully automatic inspection system is designed, which actively selects the most salient carbide structure on the specimen surface for subsequent classification, and it is shown how the presented classification scheme allows for the definition of a new reference chart in terms of quantitative measures.
Journal ArticleDOI
Subspace based feature selection for pattern recognition
Serkan Gunal,Rifat Edizkan +1 more
TL;DR: This paper proposes subspace based separability measures to determine the individual discriminatory power of the features and these measures are then employed to sort and select features in a multi-class manner.
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.