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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Proceedings ArticleDOI
A Novel Feature Selection Approach for Intrusion Detection Data Classification
TL;DR: A hybrid feature selection algorithm in combination with wrapper and filter selection processes is designed and shows that the hybrid model is promising in detection compared to the previously reported results.
Proceedings ArticleDOI
Heart Disease Prediction System Using Model Of Machine Learning and Sequential Backward Selection Algorithm for Features Selection
Amin Ul Haq,Jianping Li,Muhammad Hammad Memon,Muhammad Hunain Memon,Jalaluddin Khan,Syeda Munazza Marium +5 more
TL;DR: The experimental results shows that Sequential Backward Selection (SBS) algorithms choose appropriate features and these features increase the accuracy using K-Nearest Neighbor supervised machine learning classifier.
Proceedings ArticleDOI
Detection of depression in speech
TL;DR: The motivation is to find out a speech feature set to detect, evaluate and even predict depression, and extracts features as many as possible according to previous research to create a large voice feature set.
Book ChapterDOI
Image Categorization Using ESFS: A New Embedded Feature Selection Method Based on SFS
TL;DR: This paper introduces a novel embedded feature selection method, called ESFS, which is inspired from the wrapper method SFS since it relies on the simple principle to add incrementally most relevant features, leading to a lower computational cost than original SFS.
Journal ArticleDOI
Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal.
TL;DR: This study suggests that automatic OSA recognition from single-lead ECG signals is possible, which can be used as an inexpensive and low complexity burden alternative to more conventional methods such as Polysomnography.
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.