.png){kind=icon}
返回
SVM classification for imbalanced data sets using a multiobjective optimization framework
.png){kind=icon}
PRE
AI
DOI：10.1007/s10479-012-1300-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Classification of imbalanced data sets in which negative instances outnumber the positive instances is a significant challenge. These data sets are commonly encountered in real-life problems. However, performance of well-known classifiers is limited in such cases. Various solution approaches have been proposed for the class imbalance problem using either data-level or algorithm-level modifications. Support Vector Machines (SVMs) that have a solid theoretical background also encounter a dramatic decrease in performance when the data distribution is imbalanced. In this study, we propose an L-1-norm SVM approach that is based on a three objective optimization problem so as to incorporate into the formulation the error sums for the two classes independently. Motivated by the inherent multi objective nature of the SVMs, the solution approach utilizes a reduction into two criteria formulations and investigates the efficient frontier systematically. The results indicate that a comprehensive treatment of distinct positive and negative error levels may lead to performance improvements that have varying degrees of increased computational effort.
Keyword:
SVM
Imbalanced data
Multiobjective optimization
Efficient frontier
期刊
IF:
4.5
论文数:
8.0K
被引数:
2.1W
机构
引用论文
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Algorithm robust for the bicriteria discrete optimization problem - Heuristic variations and computational evidence算法对双准则离散优化问题的鲁棒性-启发式变化和计算证据
PREAI
分享收藏
分享收藏
查看更多内容