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Image classification technology and performance based on Deep Learning havealready achieved high standards. Nevertheless, many efforts have conducted toimprove the stability of classification via ensembling. However, the existingensemble method has a limitation in that it requires extra effort includingtime consumption to find the weight for each model output. In this paper, wepropose a simple but improved ensemble method, naming with Self-WeightedEnsemble (SWE), that places the weight of each model via its verificationreliability. The proposed ensemble method, SWE, reduces overall efforts forconstructing a classification system with varied classifiers. The performanceusing SWE is 0.033% higher than the conventional ensemble method. Also, thepercent of performance superiority to the previous model is up to 73.333%(ratio of 8:22).

Self-Weighted Ensemble Method to Adjust the Influence of Individual Models based on Reliability