首页|An investigation into the effects of label noise on Dynamic Selection algorithms
An investigation into the effects of label noise on Dynamic Selection algorithms
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
In the literature on classification problems, it is widely discussed how the presence of label noise can bring about severe degradation in performance. Several works have applied Prototype Selection techniques, Ensemble Methods, or both, in an attempt to alleviate this issue. Nevertheless, these methods are not always able to sufficiently counteract the effects of noise. In this work, we investigate the effects of noise on a particular class of Ensemble Methods, that of Dynamic Selection algorithms, and we are especially interested in the behavior of the Fire-DES++ algorithm, a state of the art algorithm which applies the Edited Nearest Neighbors (ENN) algorithm to deal with the effects of noise and imbalance. We propose a method which employs multiple Dynamic Selection sets, based on the Bagging-IH algorithm, which we dub Multiple-Set Dynamic Selection (MSDS), in an attempt to supplant the ENN algorithm on the filtering step. We find that almost all methods based on Dynamic Selection are severely affected by the presence of label noise, with the exception of the K-Nearest Oracles-Union algorithm. We also find that our proposed method can alleviate the issues caused by noise in some scenarios. We have made the code for our method available at https://github.com/fnw/baggingds.
NSTL
Elsevier
