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
Review of soft sensor methods for regression applications
TL;DR: A literature review on each of the main issues of SSR development, including the treatment of missing data, outlier detection, selection of input variables, model training, validation, and SSR maintenance will be performed.
Journal ArticleDOI
Automatic Classification for Pathological Prostate Images Based on Fractal Analysis
Po-Whei Huang,Cheng-Hsiung Lee +1 more
TL;DR: A computer-aided system to automatically grade pathological images according to Gleason grading system which is the most widespread method for histological grading of prostate tissues is presented and two feature extraction methods based on fractal dimension are proposed to analyze variations of intensity and texture complexity in regions of interest.
Proceedings ArticleDOI
FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems
Xue-wen Chen,Michael Wasikowski +1 more
TL;DR: A new feature selection method, Feature Assessment by Sliding Thresholds (FAST), which is based on the area under a ROC curve generated by moving the decision boundary of a single feature classifier with thresholds placed using an even-bin distribution, is proposed.
Journal ArticleDOI
Improved binary particle swarm optimization using catfish effect for feature selection
TL;DR: Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.
Journal ArticleDOI
Test–Retest Reproducibility Analysis of Lung CT Image Features
Yoganand Balagurunathan,Virendra Kumar,Yuhua Gu,Jongphil Kim,Hua Wang,Ying Liu,Dmitry B. Goldgof,Lawrence O. Hall,Rene Korn,Binsheng Zhao,Lawrence H. Schwartz,Satrajit Basu,Steven A. Eschrich,Robert A. Gatenby,Robert J. Gillies +14 more
TL;DR: Test–retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range, making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.
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.