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
Automatic ECG-Based Emotion Recognition in Music Listening
TL;DR: An automatic ECG-based emotion recognition algorithm to recognize human emotions elicited by listening to music and a sequential forward floating selection-kernel-based class separability-based (SFFS-KBCS-based) feature selection algorithm to effectively select significant ECG features associated with emotions.
Journal ArticleDOI
Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties
TL;DR: It is suggested that QSARs exploration of NP-cell association data, considering the role of both NP protein corona and physicochemical properties, can support the planning and interpretation of toxicity studies and guide the design of NPs for biomedical applications.
Journal ArticleDOI
Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia
Rajamanickam Yuvaraj,Murugappan Murugappan,U. Rajendra Acharya,Hojjat Adeli,Norlinah Mohamed Ibrahim,Edgar Mesquita +5 more
TL;DR: Findings suggest that decrease in the functional connectivity indices during emotional stimulation in PD, indicating functional disconnections between cortical areas.
Journal ArticleDOI
Deep learning and transfer learning features for plankton classification
Alessandra Lumini,Loris Nanni +1 more
TL;DR: This work studies both the fine tuning of several deep learned models and transfer learning from the same models with the aim of exploiting their diversity in designing an ensemble of classifiers, and shows how to combine different CNN in order to improve the performance.
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Robust Hyperspectral Classification Using Relevance Vector Machine
F A Mianji,Yun Zhang +1 more
TL;DR: The capabilities of a feature reduction technique used for discrimination are combined with the advantages of a Bayesian learning-based probabilistic sparse kernel model, the relevance vector machine (RVM), to develop a new supervised classification method.
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.
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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.