论文信息 - Ensemble Learning Approaches

Ensemble Learning Approaches

As mentioned in Chap. 1, ensemble learning is helpful to improve overall accuracy of classification. This chapter introduces three approaches of ensemble learning namely, parallel learning, sequential learning and hybrid learning. In particular, some popular methods for ensemble learning, such as Bagging and Boosting, are illustrated in detail. These methods are also discussed comparatively with respects to their advantages and disadvantages.

[1] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.

[2] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[3] Leo Breiman,et al. Random Forests , 2001, Machine Learning.

[4] Daniel T. Larose,et al. Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[5] Han Liu,et al. Hybrid ensemble learning approach for generation of classification rules , 2015, 2015 International Conference on Machine Learning and Cybernetics (ICMLC).

[6] Yoav Freund,et al. A Short Introduction to Boosting , 1999 .

[7] Igor Kononenko,et al. Machine Learning and Data Mining: Introduction to Principles and Algorithms , 2007 .

[8] Han Liu,et al. Collaborative decision making by ensemble rule based classification systems , 2015 .

[9] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.