.png){kind=icon}
返回
Bagging with adaptive costs
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2007.190724.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Ensemble methods have proven to be highly effective in improving the performance of base learners under most circumstances. In this paper, we propose a new algorithm that combines the merits of some existing techniques, namely, bagging, arcing, and stacking. The basic structure of the algorithm resembles bagging. However, the misclassification cost of each training point is repeatedly adjusted according to its observed out-of-bag vote margin. In this way, the method gains the advantage of arcing-building the classifier the ensemble needs-without fixating on potentially noisy points. Computational experiments show that this algorithm performs consistently better than bagging and arcing with linear and nonlinear base classifiers'. In view of the characteristics of bacing, a hybrid ensemble learning strategy, which combines bagging and different versions of bacing, is proposed and studied empirically.
Keyword:
data mining
ensemble methods
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
分享收藏Thiol-functionalized silica colloids, grains, and membranes for irreversible adsorption of metal(oxide) nanoparticles用于金属 (氧化物) 纳米颗粒不可逆吸附的硫醇官能化二氧化硅胶体，颗粒和膜
OAAI
OAAI分享收藏分享收藏分享收藏An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization.png)
MACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏分享收藏Pentagonal-bipyramidal molybdenum- and tungsten-(II) derivatives: crystal and molecular structure of 1–3-η-allyldicarbonylchlorobis-(trimethyl phosphite)molybdenum(II)五边形-双锥体钼-和钨-(II) 衍生物: 1-3-η-烯丙基二羰基氯-(亚磷酸三甲酯) 钼 (II) 的晶体和分子结构
PREAI
分享收藏分享收藏
查看更多内容