Open AccessThe Optimization of Multi-classifier Ensemble Method Based on Dynamic Weighted Voting
Author(s) -
Ping Yang,
Jian Fang,
Jiameng Xu,
Guanghao Jin,
Qing Song
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2185/1/012030
Subject(s) - weighted voting , voting , computer science , classifier (uml) , artificial intelligence , data mining , pattern recognition (psychology) , majority rule , machine learning , politics , political science , law
Generally, on the same data set, different deep learning classification models will achieve different performances. The existing weighted voting method can combine the results of models, which can improve the performance of classification. On the other side, its classification accuracy is affected by the accuracy of all models. In this paper, we proposed a dynamic weighted voting method. Our method dynamically selects models on different data sets, and integrates them according to their weights, thereby improving the classification accuracy. We evaluated the methods on three data sets of CIFAR10, CIFAR100 and Existing, which increased the accuracy about 0.65%, 0.91%, and 0.78% respectively compared with the existing weighted voting method.