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
An efficient statistical feature selection approach for classification of gene expression data
B. Chandra,Manish Gupta +1 more
TL;DR: The proposed feature selection algorithm can be helpful in ranking the genes and also is capable of identifying the most relevant genes responsible for diseases like leukemia, colon tumor, lung cancer, diffuse large B-cell lymphoma, prostate cancer.
Journal ArticleDOI
Feature selection using Forest Optimization Algorithm
TL;DR: The proposed FSFOA is validated on several real world datasets and it is compared with some other methods including HGAFS, PSO and SVM-FuzCoc, which shows improvement in classification accuracy of classifiers in some datasets.
Journal ArticleDOI
Feature subset selection in unsupervised learning via multiobjective optimization
Julia Handl,Joshua Knowles +1 more
TL;DR: The problem of unsupervised feature selection and its formulation as a multiobjective optimization problem are investigated and an algorithmic framework encompassing both wrapper and filter methodsoffeatureselection is used.
Journal ArticleDOI
Soft fuzzy rough sets for robust feature evaluation and selection
Qinghua Hu,Shuang An,Daren Yu +2 more
TL;DR: It is shown that the fuzzy dependency function proposed in the fuzzy rough set model is not robust to noisy information in this paper, and a new dependence function is constructed from the model which can reduce the influence of noise.
Journal ArticleDOI
Oil spill feature selection and classification using decision tree forest on SAR image data
TL;DR: In this article, a novel oil spill feature selection and classification technique is presented, based on a forest of decision trees, and the parameters of the two-class classification problem of oil spills and look-alikes are explored.
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.