Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
On combining classifiers using sum and product rules
Pattern Recognition Letters
High-Order Pattern Discovery from Discrete-Valued Data
IEEE Transactions on Knowledge and Data Engineering
From Association to Classification: Inference Using Weight of Evidence
IEEE Transactions on Knowledge and Data Engineering
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
EROS: Ensemble rough subspaces
Pattern Recognition
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Combining multiple classifiers is expected to increase classification accuracy. Research on combination strategies of multiple classifiers becomes a popular topic. For a crisp classifier, which returns a discrete class label instead of a set of real-valued probabilities respecting to every classes, the often used combination method is majority voting. Both majority and weighted majority voting are classifier-based voting schemes, which provide a certain base classifier with an identical confidence in voting. However, each classifier should have different voting priorities with respect to its learning space. This differences can not be reflected by classifier-based voting strategy. In this paper, we propose another two voting strategies in an effort to take such differences into consideration. We apply the AdaBoost algorithm to generate multiple classifiers and vary its voting strategy. Then, the prediction ability of each voting strategy is tested and compared on 8 datasets taken from UCI Machine Learning Repository. The experimental results show that one of the proposed voting strategies, namely sample-based voting scheme, achieves better performance in view of classification accuracy.