Title: Machine learning method based on improved drosophila optimisation algorithm

Authors: Wang Haiying

Addresses: Department of Big Data and Computer Science, NorthEeast Petroleum University, Qin Huangdao 066004, China

Abstract: Aiming at the problems of poor classification effect and high CPU ratio of traditional machine learning methods, a machine learning method based on improved drosophila optimisation algorithm was proposed. The rank one data mapping and the low order data are established. In low rank support vector set, CP rank organisation of traditional support vector machine is used to improve data security. The traditional drosophila algorithm was improved and optimised to increase the number of data iterations, ensure the compatibility of rank one data, improve the optimal calculation of drosophila, and increase the density clustering. The decomposition process is designed to evaluate the objective function value of the optimal solution. In the evaluation process, support vector machine is used to complete the label classification of learning data. Experimental data show that this method performs well in data classification effect, low-rank data storage dimension characteristic performance and CPU operation proportion performance.

Keywords: machine learning; drosophila algorithm; low rank data; support vector machine.

DOI: 10.1504/IJICT.2021.113039

International Journal of Information and Communication Technology, 2021 Vol.18 No.2, pp.142 - 159

Received: 20 Sep 2019
Accepted: 11 Nov 2019
Published online: 16 Feb 2021 *

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