Earticle

초록

영어

Data imbalance problem is common and causes serious problem in machine learning process. Sampling is one of the effective methods for solving data imbalance problem. Over-sampling increases the number of instances, so when over-sampling is applied in imbalanced data, it is applied to minority instances. Under-sampling reduces instances, which usually is performed on majority data. We apply under-sampling and over-sampling to imbalanced data and generate sampled data sets. From the generated data sets from sampling and original data set, we construct a heterogeneous ensemble of classifiers. We apply five different algorithms to the heterogeneous ensemble. Experimental results on an intrusion detection dataset as an imbalanced datasets show that our approach shows effective results.

목차

Abstract
1. Introduction
2. Data imbalance problem
2.1 Over-sampling
2.2 Under-sampling
2.3 Fractional instances
2.4 Cost for misclassification
2.5 Imbalance-aware learning algorithms
3. Heterogeneous ensemble
3.1 Bagging
3.2 Boosting
3.3 Arcing
3.4 Stacking
4. Our approach
5. Experiments
5.1 Datasets
5.2 Experimental Results
6. Conclusion
Acknowledgement
References

키워드

저자

• Dae-Ki Kang [ Dept. of Computer Engineering, Dongseo University, Busan, South Korea ]
• Min-gyu Han [ ICT Convergence Program, Hansung University, Seoul, South Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380