A First Study on the Use of Boosting for Class Noise Reparation
نویسندگان
چکیده
Class noise refers to the incorrect labeling of examples in classification, and is known to negatively affect the performance of classifiers. In this contribution, we propose a boosting-based hybrid algorithm that combines data removal and data reparation to deal with noisy instances. A experimental procedure to compare its performance against no-preprocessing is developed and analyzed, laying the foundations for future works.
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تاریخ انتشار 2016