Combining Multiple Classifiers with Dynamic Weighted Voting
نویسندگان
چکیده
When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In the present paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results with several real-problem data sets from the UCI Machine Learning Database Repository demonstrate the advantages of these novel weighting strategies over the simple voting scheme.
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