This paper reviews ordinal decision tree algorithms for ordinal classification, exploring theoretical foundations, key algorithms (MDT, QMDT), specialized splitting criteria (Ordinal Gini, Weighted Information Gain), and ensemble methods. It discusses applications in healthcare and social sciences, highlighting interpretability and flexibility while acknowledging overfitting and instability. As implications for future research, this study points out advantages such as interpretability and flexibility, and limitations such as overfitting and instability.

Abstract
1. 서 론
2. 의사 결정 트리 의 이론적 기반
    2.1 의사 결정 트리 의 기본 구조와 유형
    2.2 표준 분할 기준
    2.3 순서형 데이터에 대한 표준 의사 결정 트리 의 한계
3. 순서형 의사 결정 트리 알고리 즘
    3.1 순서형 분류의 정의와 중요성
    3.2 의사 결정 트리 학습에서의 단조성 개념
    3.3 특정 순서형 의사 결정 트리 알고리 즘
    3.4 순서형 속성 및 목 표 변수를 위한 특화된분할 기준
4. 성능 향상을 위한 앙상블 방법
    4.1 배깅 및 랜덤 포레스트 적용
    4.2 그래디언트 부스팅 기법
    4.3 앙상블 트리 모 델에 단조성 제약 통합
5. 논의 및 결론
References

• 변해원(Dept. of Future Technology, Korea University of Technology and Education, South Korea) | Haewon Byeon Corresponding author