An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

Abstract

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.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…Similarly, Qian et al [ 190 ] proposed an upgraded combined feature selection algorithm comprised of nonlinear inertia weight binary particle swarm optimization with shrinking encircling and exploration mechanism (NBPSOSEE) with sequential backward selection (SBS) known as NBPSOSEE-SBS, to select the best feature subset and applied in electric charge recovery risk. The experiment results proved the effectiveness of NBPSOSEE-SBS in reducing the significant number of irrelevant features and improving the prediction results in terms of the lower execution time compared with a well-known algorithm with seven other popular wrapper-based features subset selection techniques used in the prediction of risk of ECR for power customers.…”