Data filter function incremental mining based on feature selection in an active distribution network

Abstract

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.

Keywords

• data mining
• security of data
• massive data
• high dimensional data
• data filtering function model
• function model mining
• incremental dataset
• incremental mining algorithm
• data filter function incremental mining
• active distribution network
• traditional distributed networks
• complex access environment
• flexible access mode
• massive access terminal
• great security challenges
• data transmission
• existing data security methods
• access control
• nontext data
• feature selection algorithm