원문정보
초록
영어
The classification of imbalanced data is one of the most challenging problems in data mining and machine learning research. Imbalanced dataset is a form that exists in reality area, which describes truly and objectively the essential characters of something. There will appear paucity of data and flooded in the classification of imbalanced dataset. Beside problems such as loss of information and data splitting phenomenon will also appear when using the traditional machine learning methods. So how to solve the classification problem of imbalanced data will be challenging. In this paper, aiming at the above problems, a classification algorithm based on AdaBoost-SVM is proposed. In the experiments with four typical forms of imbalanced data sets in UCI were validated the effectiveness of this strategy.
목차
1. Introduction
2. Methodology
2.1. Imbalanced Data Classification
2.2. Classification Algorithm
3. The Results and analysis of experiment
3.1. Evaluation Index of Experiment
3.2. Experimental Results and Analysis
4. Conclusion
Acknowledgements
References