An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk
Joint Authors
Qian, Shenyi
Shi, Yongsheng
Wu, Huaiguang
Shang, Songtao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-28
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
In order to extract more information that affects customer arrears behavior, the feature extraction method is used to extend the low-dimensional features to the high-dimensional features for the warning problem of user arrears risk model of electric charge recovery (ECR).
However, there are many irrelevant or redundant features in data, which affect prediction accuracy.
In order to reduce the dimension of the feature and improve the prediction result, an improved hybrid feature selection algorithm is proposed, integrating nonlinear inertia weight binary particle swarm optimization with shrinking encircling and exploration mechanism (NBPSOSEE) with sequential backward selection (SBS), namely, NBPSOSEE-SBS, for selecting the optimal feature subset.
NBPSOSEE-SBS can not only effectively reduce the redundant or irrelevant features from the feature subset selected by NBPSOSEE but also improve the accuracy of classification.
The experimental results show that the proposed NBPSOSEE-SBS can effectively reduce a large number of redundant features and stably improve the prediction results in the case of low execution time, compared with one state-of-the-art optimization algorithm, and seven well-known wrapper-based feature selection approaches for the risk prediction of ECR for power customers.
American Psychological Association (APA)
Qian, Shenyi& Shi, Yongsheng& Wu, Huaiguang& Shang, Songtao. 2020. An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1201162
Modern Language Association (MLA)
Qian, Shenyi…[et al.]. An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk. Mathematical Problems in Engineering No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1201162
American Medical Association (AMA)
Qian, Shenyi& Shi, Yongsheng& Wu, Huaiguang& Shang, Songtao. An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1201162
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201162