2010 TheEffectivenessofaNewNegativeC

• (Wang & Yao, 2010) ⇒ Shuo Wang, and Xin Yao. (2010). “The Effectiveness of a New Negative Correlation Learning Algorithm for Classification Ensembles.” In: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops. ISBN:978-0-7695-4257-7 doi:10.1109/ICDMW.2010.196

Subject Headings: Negative Correlation Learning.

Cited By

• http://scholar.google.com/scholar?q=%222010%22+The+Effectiveness+of+a+New+Negative+Correlation+Learning+Algorithm+for+Classification+Ensembles
• http://dl.acm.org/citation.cfm?id=1933306.1934424&preflayout=flat#citedby

Quotes

Author Keywords

• Correlation Methods; Learning (Artificial Intelligence), Pattern Classification, Negative Correlation Learning Algorithm, Classification Ensembles, AdaBoost, NCL Algorithms, Penalty Strength, Error Correlation, Training, Correlation, Accuracy, Context, Classification Algorithms, Error Analysis, Ensemble Learning, Classification, Negative Correlation Learning, Diversity.

Abstract

In an earlier paper, we proposed a new negative correlation learning (NCL) algorithm for classification ensembles, called AdaBoost.NC, which has significantly better performance than the standard AdaBoost and other NCL algorithms on many benchmark data sets with low computation cost. In this paper, we give deeper insight into this algorithm from both theoretical and experimental aspects to understand its effectiveness. We explain why AdaBoost.NC can reduce error correlation within the ensemble and improve the classification performance. We also show the role of the λ (penalty) term in the training error. Finally, we examine the effectiveness of AdaBoost.NC by varying two pre-defined parameters€“ penalty strength λ and ensemble size T. Experiments are carried out on both artificial and real-world data sets, which show that AdaBoost.NC does produce smaller error correlation along with training epochs, and a lower test error comparing to the standard AdaBoost. The optimal λ depends on problem domains and base learners. The performance of AdaBoost.NC becomes stable as T gets larger. It is more effective when T is comparatively small.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 TheEffectivenessofaNewNegativeC	Xin Yao Shuo Wang			The Effectiveness of a New Negative Correlation Learning Algorithm for Classification Ensembles				10.1109/ICDMW.2010.196		2010