.png){kind=icon}
返回
Automated imbalanced classification via layered learning
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-022-06282-w.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find instances of interest close to the decision boundary to drive the resampling process. However, under-sampling the majority class may potentially lead to important information loss. Over-sampling also may increase the chance of overfitting by propagating the information contained in instances from the minority class. The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations. Instead, ICLL follows a layered learning paradigm to model the data in two stages. In the first layer, ICLL learns to distinguish cases close to the decision boundary from cases which are clearly from the majority class, where this dichotomy is defined using a hierarchical clustering analysis. In the subsequent layer, we use instances close to the decision boundary and instances from the minority class to solve the original predictive task. A second contribution of our work is the automatic definition of the layers which comprise the layered learning strategy using a hierarchical clustering model. This is a relevant discovery as this process is usually performed manually according to domain knowledge. We carried out extensive experiments using 100 benchmark data sets. The results show that the proposed method leads to a better performance relatively to several state-of-the-art methods for IBC.
Keyword:
Imbalanced classification
Layered learning
Hierarchical clustering
期刊
IF:
2.9
论文数:
2.7K
被引数:
3.4W
机构
引用论文
分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Neuronal-binding antibodies from patients with antiphospholipid syndrome induce cognitive deficits following intrathecal passive transferLupus
LupusIF0
PREAI
分享收藏Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning在mdp和半mdp之间: 强化学习中的时间抽象框架
OAAI
OAAI分享收藏
查看更多内容