Testing Coverage Criteria for Deep Forests

Xie, Ruilin; Cui, Zhanqi; Jia, Minghua; Yuan, Wen; Bao-shui, Hao

doi:10.1109/dsa.2019.00091

Cited by 1 publication

(1 citation statement)

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“…The combined base model has four performance indicators: Recall, MAP@100, F 1 -score, and AUC under the new feature set Y after preprocessing, feature extraction and dimensionality reduction of the SGCC dataset, at this time, the meta-model of the stacking structure chooses a relatively simple linear regression (LR) model [38]. According to the classifier, selection of the base model layer, as in Section 3.3, should be strong and numerous, so the performance index values of eight existing classifiers commonly used for electricity theft detection under the new feature set Y are compared, and the eight classifiers are: random forest (RF) [39], eXtreme gradient boosting (XGBoost) [25], light gradient boosting machine (LightGBM) [40], support vector machine (SVM) [22], CART decision tree (DT) [23], deep forest (DF) [41], long short-term memory (LSTM) [28], and K-nearest neighbor (KNN) [42].…”

Section: Construction Of Base Model Layer In Stacking Structurementioning

confidence: 99%

An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis

et al. 2022

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Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675).

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