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
Proceedings ArticleDOI
Retrieving categorical emotions using a probabilistic framework to define preference learning samples
Reza Lotfian,Carlos Busso +1 more
TL;DR: A probabilistic framework that creates relative labels from existing categorical annotations that quantifies the likelihood that a sample belong to a target emotion is proposed, and improved performance over binary classifiers and rank-based classifiers trained with consensus labels is evaluated.
Journal ArticleDOI
Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Wei Lin Ng,Kartini Rahmat,Vidya K. Sudarshan,Joel E.W. Koh,Jen Hong Tan,Yuki Hagiwara,Chai Hong Yeong,Kwan Hoong Ng +10 more
TL;DR: A unique integrated index named Shear Wave Breast Cancer Risk Index (sBCRI) is formulated for characterization of malignant and benign breast lesion using only two features, and can be employed as an ideal screening tool as it has high sensitivity and low false-positive rate.
Journal ArticleDOI
Compressive sensing resonator spectroscopy.
TL;DR: A new fast compressive spectroscopic technique based on the resonance spectrometric mechanism that acquires different multiplexed spectral modulations from which the original signal is reconstructed using a compressive sensing reconstruction algorithm.
Journal ArticleDOI
Impact of contact pressure–induced spectral changes on soft-tissue classification in diffuse reflectance spectroscopy: problems and solutions
TL;DR: Three practical guidelines have been proposed to avoid classification performance degradation in biomedical applications involving common probe-spectrometer diffuse reflectance measurement setups, where the contact pressure was precisely controlled, and the spectral and contact pressure information were acquired simultaneously.
Book ChapterDOI
Boosting feature selection
D. B. Redpath,K. Lebart +1 more
TL;DR: The proposed AdaboostFS algorithm produces a significant reduction in the number of features required for classification in each base classifier and the entire ensemble.
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.