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
Driver Sleepiness Classification Based on Physiological Data and Driving Performance From Real Road Driving
TL;DR: If signal analysis and machine learning can be used to develop an accurate sleepiness warning system is investigated, and a random forest classifier was found to be the most robust classifier with an accuracy of 94.1%.
Proceedings ArticleDOI
Realisable Classifiers: Improving Operating Performance on Variable Cost Problems.
TL;DR: A novel method is described for obtaining superior classification performance over a variable range of classification costs by analysis of a set of existing classifiers using a receiver operating characteristic and a new system is shown to produce the , a powerful technique for improving classification systems in problem domains within which classification costs may not be known.
Journal ArticleDOI
Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data
Camile Sothe,C. M. de Almeida,Marcos Benedito Schimalski,Laura Elena Cué La Rosa,Jose David Bermudez Castro,Raul Queiroz Feitosa,Michele Dalponte,Carla Luciane Lima,Veraldo Liesenberg,Gabriela Takahashi Miyoshi,Antonio Maria Garcia Tommaselli +10 more
TL;DR: Innovative strategies for classifying tree species in subtropical forest areas at a refined legend level are presented, integrating UAV-borne 2D hyperspectral and 3D photogrammetric data and relying on both deep and conventional machine learning approaches.
Book ChapterDOI
Feature selection using ant colony optimization (ACO): a new method and comparative study in the application of face recognition system
TL;DR: This paper presents a novel feature selection method that is based on Ant Colony Optimization (ACO), inspired of ant's social behavior in their search for the shortest paths to food sources and its computational complexity is very low.
Proceedings ArticleDOI
Fast sequential floating forward selection applied to emotional speech features estimated on DES and SUSAS data collections
TL;DR: This paper classifies speech into several emotional states based on the statistical properties of prosody features estimated on utterances extracted from Danish Emotional Speech and a subset of Speech Under Simulated and Actual Stress data collections, demonstrating that gender and accent information reduce the classification error.
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.