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
More filters
Journal ArticleDOI
Improving the accuracy of computer-aided radiographic weld inspection by feature selection
TL;DR: Two versions of ant colony optimization (ACO)-based algorithms for feature selection are proposed and their effectiveness to improve the accuracy in detecting weld flaws and the accuracy of classifying weld flaw types is shown.
Proceedings ArticleDOI
Automatic classification of retinal vessels into arteries and veins
TL;DR: This work presents a supervised, automatic method that can determine whether a vessel is an artery or a vein based on intensity and derivative information.
Journal ArticleDOI
Leveraging TSP Solver Complementarity through Machine Learning
TL;DR: This work directly compares five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrates complementary performance, in that different instances may be solved most effectively by different algorithms.
Journal ArticleDOI
Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine
TL;DR: A hybrid optimization technique is introduced to symptom selection for multi-label data in TCM diagnosis in this paper, and modeling is made by means of four multi- label learning algorithms like k nearest neighbors, etc.
Journal ArticleDOI
Dynamic Learning of SMLR for Feature Selection and Classification of Hyperspectral Data
TL;DR: This letter introduces the sparse multinomial logistic regression (SMLR) into the community of remote sensing and is utilized for the feature selection in the classification of hyperspectral data and develops a dynamic learning framework to train the SMLR.
References
More filters
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.