Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is pro...

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Autores principales:	Tajanpure Rupali, Muddana Akkalakshmi
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	high-dimensional data convolution dimensionality reduction feature extraction Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/e2cd7872f64c48ff9166c079fd85fa5f
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id	oai:doaj.org-article:e2cd7872f64c48ff9166c079fd85fa5f
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spelling	oai:doaj.org-article:e2cd7872f64c48ff9166c079fd85fa5f2021-12-05T14:10:51ZCircular convolution-based feature extraction algorithm for classification of high-dimensional datasets2191-026X10.1515/jisys-2020-0064https://doaj.org/article/e2cd7872f64c48ff9166c079fd85fa5f2021-10-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0064https://doaj.org/toc/2191-026XHigh-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is tested using the decision tree (DT), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy (Δ) is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy.Tajanpure RupaliMuddana AkkalakshmiDe Gruyterarticlehigh-dimensional dataconvolutiondimensionality reductionfeature extractionScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 1026-1039 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	high-dimensional data convolution dimensionality reduction feature extraction Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	high-dimensional data convolution dimensionality reduction feature extraction Science Q Electronic computers. Computer science QA75.5-76.95 Tajanpure Rupali Muddana Akkalakshmi Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets
description	High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is tested using the decision tree (DT), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy (Δ) is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy.
format	article
author	Tajanpure Rupali Muddana Akkalakshmi
author_facet	Tajanpure Rupali Muddana Akkalakshmi
author_sort	Tajanpure Rupali
title	Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets
title_short	Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets
title_full	Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets
title_fullStr	Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets
title_full_unstemmed	Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets
title_sort	circular convolution-based feature extraction algorithm for classification of high-dimensional datasets
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/e2cd7872f64c48ff9166c079fd85fa5f
work_keys_str_mv	AT tajanpurerupali circularconvolutionbasedfeatureextractionalgorithmforclassificationofhighdimensionaldatasets AT muddanaakkalakshmi circularconvolutionbasedfeatureextractionalgorithmforclassificationofhighdimensionaldatasets
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Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets

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