Volume 3, Issue 3, May 2018, Page: 67-76
Research on Feature Selection in Power User Identification
Qiu Yanhao, College of Engineering, Virginia Polytechnic Institute and State University, Virginia, The United States
Song Xiaoyu, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China
Sun Xiangyang, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China
Zhao Yang, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China
Received: Apr. 18, 2018;       Accepted: May 7, 2018;       Published: Jun. 1, 2018
DOI: 10.11648/j.mcs.20180303.11      View  2019      Downloads  148
In the previous study of user identification, most of the researchers improved the recognition algorithm. In this paper, we use large data technology to extract electricity feature from different angles and study the impact of different features on recognition. Firstly, the raw data was cleaned. In order to obtain the key information of power theft user identification, the features of the data set are extracted from three aspects: basic attribute feature, statistical feature under different time scale and similarity feature under different time scale. Then we use feature sets of different combinations to carry out experiments under the KNN model, the random forest (RF) model and the XGBoost model. The experimental results show that the experimental results of the BF+SF+PF feature set in the three classifiers are obviously better than the other two feature sets. Therefore, it is concluded that different features have obvious effects on the recognition results.
Feature Selection, Power User Identification, KNN, Random Forest, XG Boost
To cite this article
Qiu Yanhao, Song Xiaoyu, Sun Xiangyang, Zhao Yang, Research on Feature Selection in Power User Identification, Mathematics and Computer Science. Vol. 3, No. 3, 2018, pp. 67-76. doi: 10.11648/j.mcs.20180303.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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