Open AccessData filter function incremental mining based on feature selection in an active distribution network
Author(s) -
Deng Song,
Cai Qingyuan,
Zhang Zi,
Yang Lechan,
Huang Tinglei,
Yuan Changan
Publication year - 2020
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/iet-cps.2019.0094
Subject(s) - data mining , computer science , feature selection , filter (signal processing) , encryption , network security , function (biology) , feature (linguistics) , selection (genetic algorithm) , artificial intelligence , machine learning , linguistics , philosophy , evolutionary biology , computer vision , biology , operating system
Compared with traditional distributed networks, the complex access environment, flexible access mode, massive access terminal, and data in an active distribution network will bring great security challenges to data transmission. The existing data security methods, such as access control and encryption, address the security of massive, high dimensional, and non‐text data in the active distribution network. Therefore, feature selection algorithm based on rough set is first given to reduce the complexity of massive and high dimensional data. And then, based on feature selection, the authors propose a data filtering function model mining algorithm by using gene expression programming (DFFM‐FSGEP). Finally, to solve the data filter function model mining of the incremental dataset, they also present an incremental mining algorithm of the filtering function model based on functional fitting (IMFFM‐FF). Experimental results show that the proposed algorithm in this study can greatly reduce the complexity of experimental datasets to be processed, and compared with the other algorithms, DFFM‐FSGEP has higher classification accuracy and sensitivity, and IMFFM‐FF has higher classification speed and classification accuracy for incremental datasets.