  Image classification technology and performance based on Deep Learning have
already achieved high standards. Nevertheless, many efforts have conducted to
improve the stability of classification via ensembling. However, the existing
ensemble method has a limitation in that it requires extra effort including
time consumption to find the weight for each model output. In this paper, we
propose a simple but improved ensemble method, naming with Self-Weighted
Ensemble (SWE), that places the weight of each model via its verification
reliability. The proposed ensemble method, SWE, reduces overall efforts for
constructing a classification system with varied classifiers. The performance
using SWE is 0.033% higher than the conventional ensemble method. Also, the
percent of performance superiority to the previous model is up to 73.333%
(ratio of 8:22).
