International Journal of Circuits, Systems and Signal Processing

E-ISSN: 1998-4464
Volume 15, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 15, 2021

Title of the Paper: Algorithm for Key Classification Feature Selection of Big Data Based on Henie Theorem


Authors: Wei Wang

Pages: 1208-1213 

DOI: 10.46300/9106.2021.15.131     XML


Abstract: With the extensive application of the database system, the available data of enterprises or individuals are expanding, and the existing technology is difficult to meet the data analysis requirements of the big data age. Therefore, the selection of key classification features of big data needs to be carried out. However, when the key classification features of big data are selected by the current algorithm, the distance between the samples can not be given accurately, and there is a large error in the classification. To solve this problem, a key classification feature selection algorithm based on Henie theorem is proposed. In this algorithm, the second programming algorithm is firstly used to make the weighted distance between the intra-class and the inter-class as the quadratic term and linear term parameter in the target function, and balance the relationship between the data features and the different categories. The optimized vector is used as the weight vector to measure the contribution of the feature to the classification. According to the feature importance degree, the redundancy feature is gradually deleted, and the problem of selecting the key classification features of big data into the resolution principle is fused into the Henie theorem. The function limit and sequence limit of the key classification features of big data are obtained. Based on this, the key classification features of big data are selected. Experimental simulation shows that the proposed algorithm has higher classification accuracy and can effectively meet the needs of data analysis in the era of big data.