Comparative analysis among feature selection of sEMG signal for hand gesture classification by armband

This paper introduces a novel feature selection model for supervised interval valued data based on interval K-Means clustering. The proposed model explores two kinds of feature selection through feature clustering viz., class independent feature selection and class dependent feature selection. The former one clusters the features spread across all the samples belonging to all the classes, whereas the latter one clusters the features spread across only the samples belonging to the respective classes. Both feature selection models are demonstrated to explore the generosity of clustering in selecting the interval valued features. For clustering, the kernel of the K-means clustering has been altered to operate on interval valued data. For experimentation purpose four standard benchmarking datasets and three symbolic classifiers have been used. To corroborate the effectiveness of the proposed model, a comparative analysis against the state-of-the-art models is given and results show the superiority of the proposed model.

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Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

Archives of Metallurgy and Materials ◽

10.2478/v10172-012-0091-7 ◽

2012 ◽

Vol 57 (3) ◽

pp. 829-835 ◽

Cited By ~ 1

Author(s):

Z. Głowacz ◽

J. Kozik

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Mahalanobis Distance ◽

Distance Measure ◽

Synchronous Motor ◽

Medical Diagnostics ◽

Motor Current ◽

Feature Spaces ◽

Multidimensional Feature Spaces ◽

Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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Dynamics of export potential of sunflower oil in Ukraine

Helia ◽

10.1515/helia-2021-0001 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Kateryna Vasylkovska ◽

Olha Andriienko ◽

Oleksii Vasylkovskyi ◽

Andrii Andriienko ◽

Popov Volodymyr ◽

...

Keyword(s):

Climate Change ◽

Comparative Analysis ◽

Sunflower Oil ◽

Climate Changes ◽

Qualitative Change ◽

Sunflower Seeds ◽

Cultivation Technology ◽

Selection Of

Abstract The analysis of the production and yield of sunflower seeds in Ukraine for the period from 2000 to 2019 was conducted in the article. The comparative analysis of the gross harvest of sunflower seeds and the export of sunflower oil for the years under research was carried out. The dependence of exports on gross harvest was revealed and its share was calculated. It was determined that the export of sunflower oil has increased over the years under research, which indicates a significant Ukraine’s export potential. It was found that the increase in the share of exports by 15.9% was made possible by a qualitative change in yield, that was ensured by the changes in the cultivation technology and by the selection of sunflower hybrids that are better adapted to climate changes. The recommendations for further improvement of cultivation technology in connection with climate change in order to further increase yields and the export potential of Ukraine were given.

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