A META Learning‐Based Dynamic Selective Ensemble in Enabling Load Classification With Classifier Redundancy Reduction

The ensemble learning technologies represented by bagging show notable performance in the field of high‐performance electrical load classification researches. However, bagging frequently encounter classifier redundancy issue which significantly impacts classification accuracy. Therefore, to research a suitable base, classifier selection strategy is one of the most important directions to improve the effectiveness of ensemble learning participating in the load classification tasks. Therefore, aiming at solving the redundancy issue of the base classifiers in the bagging‐based ensemble learning, this article presents a META learning‐based dynamic selective ensemble strategy. First, the class labels of the load data samples can be achieved using the exponential similarity (Esim) distance‐based spectral clustering and k ‐medoids clustering. Second, according to the labeled load samples, an ensemble‐based back propagation neural network (BPNN) load classification model can be constructed. Afterward, a META learning‐based dynamic selective ensemble strategy of optimizing the base classifiers ensemble is presented. Specifically, META feature sets (MFSs) of base classifiers are defined and extracted. And then, a META discriminator is trained using the MFSs, which is finally able to select suitable base classifiers ensemble for the classification for each individual sample to be classified. Ultimately, case studies are carried out using the UCI Electrical Grid Stability Simulated Dataset (EGSSD) and UCI Electricity Load Diagrams 2011–2014 Dataset (ELDD). According to the experimental result, the effectiveness of presented strategy of improving the classification performance can be identified.