This study proposes a weight optimization technique based on Mixture Design of Experiments (MD) to overcome the limitations of traditional ensemble learning and achieve optimal predictive performance with minimal experimentation. Traditional ensemble learning combines the predictions of multiple base models through a meta-model to generate a final prediction but has limitations in systematically optimizing the combination of base model performances. In this research, MD is applied to efficiently adjust the weights of each base model, constructing an optimized ensemble model tailored to the characteristics of the data. An evaluation of this technique across various industrial datasets confirms that the optimized ensemble model proposed in this study achieves higher predictive performance than traditional models in terms of F1-Score and accuracy. This method provides a foundation for enhancing real-time analysis and prediction reliability in data-driven decision-making systems across diverse fields such as manufacturing, fraud detection, and medical diagnostics.

1. 서 론
2. 데이터 및 방법론
    2.1 데이터셋 설명
    2.2 앙상블 기법
    2.3 사용된 기저 모델들
    2.4 메타 모델(Meta Models)로 사용된 XG-Boost
3. 제안하는 해법
    3.1 혼합물실험계획법
    3.2 후진제거법(Backward Elimination)
    3.3 제안하는 해법
4. 실험 결과
5. 결 론
References

• Youngseok Kwon(Department of Industrial Engineering, Kongju National University) | 권영석 (공주대학교 산업공학과)
• Kwanghyun Lee(Department of Industrial Engineering, Kongju National University) | 이광현 (공주대학교 산업공학과)
• Dongju Lee(Department of Industrial Engineering, Kongju National University) | 이동주 (공주대학교 산업공학과) Corresponding author