Research on Feature Selection in Power User Identification

Qiu Yanhao; Song Xiaoyu; Sun Xiangyang; Zhao Yang

doi:doi:10.11648/j.mcs.20180303.11

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Research on Feature Selection in Power User Identification

Qiu Yanhao, Song Xiaoyu, Sun Xiangyang, Zhao Yang

Published in Mathematics and Computer Science (Volume 3, Issue 3)

Received: 18 April 2018 Accepted: 7 May 2018 Published: 1 June 2018

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Abstract

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.

Published in	Mathematics and Computer Science (Volume 3, Issue 3)
DOI	10.11648/j.mcs.20180303.11
Page(s)	67-76
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Feature Selection, Power User Identification, KNN, Random Forest, XG Boost

References

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APA Style

Qiu Yanhao, Song Xiaoyu, Sun Xiangyang, Zhao Yang. (2018). Research on Feature Selection in Power User Identification. Mathematics and Computer Science, 3(3), 67-76. https://doi.org/10.11648/j.mcs.20180303.11

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ACS Style

Qiu Yanhao; Song Xiaoyu; Sun Xiangyang; Zhao Yang. Research on Feature Selection in Power User Identification. Math. Comput. Sci. 2018, 3(3), 67-76. doi: 10.11648/j.mcs.20180303.11

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AMA Style

Qiu Yanhao, Song Xiaoyu, Sun Xiangyang, Zhao Yang. Research on Feature Selection in Power User Identification. Math Comput Sci. 2018;3(3):67-76. doi: 10.11648/j.mcs.20180303.11

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@article{10.11648/j.mcs.20180303.11,
  author = {Qiu Yanhao and Song Xiaoyu and Sun Xiangyang and Zhao Yang},
  title = {Research on Feature Selection in Power User Identification},
  journal = {Mathematics and Computer Science},
  volume = {3},
  number = {3},
  pages = {67-76},
  doi = {10.11648/j.mcs.20180303.11},
  url = {https://doi.org/10.11648/j.mcs.20180303.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20180303.11},
  abstract = {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.},
 year = {2018}
}

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TY  - JOUR
T1  - Research on Feature Selection in Power User Identification
AU  - Qiu Yanhao
AU  - Song Xiaoyu
AU  - Sun Xiangyang
AU  - Zhao Yang
Y1  - 2018/06/01
PY  - 2018
N1  - https://doi.org/10.11648/j.mcs.20180303.11
DO  - 10.11648/j.mcs.20180303.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 67
EP  - 76
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20180303.11
AB  - 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.
VL  - 3
IS  - 3
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Qiu Yanhao, Song Xiaoyu, Sun Xiangyang, Zhao Yang. (2018). Research on Feature Selection in Power User Identification. Mathematics and Computer Science, 3(3), 67-76. https://doi.org/10.11648/j.mcs.20180303.11

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ACS Style

Qiu Yanhao; Song Xiaoyu; Sun Xiangyang; Zhao Yang. Research on Feature Selection in Power User Identification. Math. Comput. Sci. 2018, 3(3), 67-76. doi: 10.11648/j.mcs.20180303.11

Copy | Download

AMA Style

Qiu Yanhao, Song Xiaoyu, Sun Xiangyang, Zhao Yang. Research on Feature Selection in Power User Identification. Math Comput Sci. 2018;3(3):67-76. doi: 10.11648/j.mcs.20180303.11

Copy | Download

@article{10.11648/j.mcs.20180303.11,
  author = {Qiu Yanhao and Song Xiaoyu and Sun Xiangyang and Zhao Yang},
  title = {Research on Feature Selection in Power User Identification},
  journal = {Mathematics and Computer Science},
  volume = {3},
  number = {3},
  pages = {67-76},
  doi = {10.11648/j.mcs.20180303.11},
  url = {https://doi.org/10.11648/j.mcs.20180303.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20180303.11},
  abstract = {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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Research on Feature Selection in Power User Identification
AU  - Qiu Yanhao
AU  - Song Xiaoyu
AU  - Sun Xiangyang
AU  - Zhao Yang
Y1  - 2018/06/01
PY  - 2018
N1  - https://doi.org/10.11648/j.mcs.20180303.11
DO  - 10.11648/j.mcs.20180303.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 67
EP  - 76
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20180303.11
AB  - 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.
VL  - 3
IS  - 3
ER  -

Copy | Download