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
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Sizeless: Predicting the optimal size of serverless functions.
TL;DR: This paper introduces an approach to predict the optimal resource size of a serverless function using monitoring data from a single resource size, which enables cloud providers to implement resource sizing on a platform level and automate the last resource management task associated with serverless functions.
Journal ArticleDOI
A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification
TL;DR: The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods.
Journal ArticleDOI
An Improved Firefly Algorithm for Feature Selection in Classification
TL;DR: In this research, one proposal was put forward, the fireflies algorithm that combines the binary firefly algorithm with opposition-based learning to select features in classification, and experiment outcomes indicate the fact that the means put forward surpasses PSO and the conventional FA.
Journal ArticleDOI
A pattern recognition approach based on DTW for automatic transient identification in nuclear power plants
Javier Galbally,David Galbally +1 more
TL;DR: A new method for automatic transient identification is proposed, based on the Dynamic Time Warping (DTW) algorithm, largely used in other related areas such as signature or speech recognition, which shows the high accuracy and the low complexity and its very limited requirements of training data.
Proceedings ArticleDOI
Dynamic Oscillating Search algorithm for feature selection
TL;DR: It is shown that the new feature selection method suitable for non-monotonic criteria, i.e., for wrapper-based feature selection, is capable of over-performing older methods not only in criterion maximization ability but in some cases also in obtaining subsets that generalize better.
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.