Reduction of Conditional Factors in Causal Analysis

  • Haitao Liu Liaoning Technical University http://orcid.org/0000-0002-0517-4718
  • Ioan Dzitac 1. Aurel Vlaicu University of Arad 310330 Arad, Elena Dragoi, 2, Romania ioan.dzitac@uav.ro 2. Agora University of Oradea 410526 Oradea, P-ta Tineretului 8, Romania,
  • Sicong Guo 1. Institute of Intelligence Engineering and Mathematics Liaoning Technical University, Fuxin 123000, China 2. College of Science Liaoning Technical University, Fuxin 123000, China

Abstract

Faced with a great number of conditional factors in big data causal analysis, the reduction algorithm put forward in this paper can reasonably reduce the number of conditional factors. Compared with the previous reduction methods, we take into consideration the influence of conditional factors on resulted factors, as well as the relationship among conditional factors themselves. The basic idea of the algorithm proposed in this paper is to establish the matrix of mutual deterministic degrees in between conditional factors. If a conditional factor f has a greater deterministic degree with respect to another conditional factor h, we will delete the factor h unless factor h has a greater deterministic degree with respect to f, then delete factor f in this case. With this reduction, we can ensure that the conditional factors participating in causal analysis are as irrelevant as possible. This is a reasonable requirement for causal analysis.

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Published
2018-05-27
How to Cite
LIU, Haitao; DZITAC, Ioan; GUO, Sicong. Reduction of Conditional Factors in Causal Analysis. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 3, p. 383-390, may 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3252>. Date accessed: 14 july 2020. doi: https://doi.org/10.15837/ijccc.2018.3.3252.

Keywords

Factors space, Causal analysis, Reduction of factors