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

TL;DR

This paper introduces a Self-Weighted Ensemble (SWE) method that assigns weights to models based on their reliability, improving classification stability with less effort and slightly better performance.

Contribution

The paper presents a novel ensemble approach that automatically adjusts model weights based on reliability, reducing the need for manual weight tuning.

Findings

SWE achieves 0.033% higher accuracy than traditional ensemble methods.

SWE's performance surpasses previous models in up to 73.333% of cases.

The method simplifies ensemble construction by leveraging model reliability.

Abstract

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).