Earticle

초록

영어

The idea of ensemble learning is to train multiple models, each with the objective to predict or classify a set of results. Most of the errors from a model’s learning are from three main factors: variance, noise, and bias. By using ensemble methods, we’re able to increase the stability of the final model and reduce the errors mentioned previously. By combining many models, we’re able to reduce the variance, even when they are individually not great. In this paper we propose an ensemble model and applied it to classification problem. In iris, Pima indian diabeit and semiconductor fault detection problem, proposed model classifies well compared to traditional single classifier that is logistic regression, SVM and random forest .

목차

Abstract
1. INTRODUCTION
2. BAGGING
2.1 DECISION TREES
2.2 DECISION TREES ALGORITHM
3. EXPERIMENT
3.1 IRIS DATA
3.2 WINE DATA
3.3 SEMICONDUCTOR DATA
4. COMPARISON WITH SVM
5. CONCLUSION
REFERENCES

키워드

저자

• ByungJoo Kim [ Department of Computer Engineering, Youngsan University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380