University of Bahrain
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NMF-DCA: An efficient dendritic cell algorithm based on non-negative matrix factorization

Show simple item record Belhadj, Mourad Cherif, Foudil Cheriet, Mohamed 2021-03-03T13:00:17Z 2021-03-03T13:00:17Z 2021-04-21
dc.identifier.issn 2210-142X
dc.description.abstract We discuss in this paper the challenge of enhancing the dendritic cell algorithm preprocessing phase. In short, to minimize data dimensionality we propose a new dendritic cell algorithm based on Non-negative Matrix Factorization. The aim of this method is to extract latent features from lower rank data transformation. The proposed method was divided in two steps. The first step is a factorization of the original data. Secondly, the new reduced space should be assigned to its respective signal category. Experimental findings show that the preprocessing step of the dendritic cell algorithm is significantly improved with respect to its execution and a higher accuracy rate. Our algorithm is also compared to other classification algorithms particularly MLP, SVM, and KNN. The comparison shows that the actual rate of current algorithms is outperformed by the proposed algorithm. en_US
dc.publisher University of Bahrain en_US
dc.subject Dendritic cell algorithm, non-negative matrix factorization, Artificial immune system en_US
dc.title NMF-DCA: An efficient dendritic cell algorithm based on non-negative matrix factorization en_US
dc.volume 10 en_US
dc.pagestart 575 en_US
dc.pageend 583 en_US
dc.contributor.authorcountry Biskra, Algeria en_US
dc.contributor.authorcountry Montréal, Canada en_US
dc.contributor.authoraffiliation LESIA Laboratory, University of Biskra en_US
dc.contributor.authoraffiliation ETS, University of Québec en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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