• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Novel Cost-Sensitive Approach to Improve the Multilayer Perceptron Performance on Imbalanced Data
【24h】

Novel Cost-Sensitive Approach to Improve the Multilayer Perceptron Performance on Imbalanced Data

机译：在不平衡数据上提高多层感知器性能的新型成本敏感方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Traditional learning algorithms applied to complex and highly imbalanced training sets may not give satisfactory results when distinguishing between examples of the classes. The tendency is to yield classification models that are biased towards the overrepresented (majority) class. This paper investigates this class imbalance problem in the context of multilayer perceptron (MLP) neural networks. The consequences of the equal cost (loss) assumption on imbalanced data are formally discussed from a statistical learning theory point of view. A new cost-sensitive algorithm (CSMLP) is presented to improve the discrimination ability of (two-class) MLPs. The CSMLP formulation is based on a joint objective function that uses a single cost parameter to distinguish the importance of class errors. The learning rule extends the Levenberg–Marquadt's rule, ensuring the computational efficiency of the algorithm. In addition, it is theoretically demonstrated that the incorporation of prior information via the cost parameter may lead to balanced decision boundaries in the feature space. Based on the statistical analysis of results on real data, our approach shows a significant improvement of the area under the receiver operating characteristic curve and $G$-mean measures of regular MLPs.
机译：区分类别示例时，应用于复杂且高度不平衡的训练集的传统学习算法可能无法获得令人满意的结果。趋势是产生偏向于代表过多（多数）的类的分类模型。本文研究了多层感知器（MLP）神经网络中的此类不平衡问题。从统计学习理论的角度正式讨论了等价（损失）假设对不平衡数据的影响。提出了一种新的成本敏感算法（CSMLP），以提高（两类）MLP的辨别能力。 CSMLP公式基于联合目标函数，该函数使用单个成本参数来区分类别错误的重要性。学习规则扩展了Levenberg–Marquadt规则，确保了算法的计算效率。另外，从理论上证明，通过成本参数合并先验信息可能会导致特征空间中决策平衡。基于对真实数据结果的统计分析，我们的方法显示了接收器工作特性曲线下的面积和常规MLP的$ G $均值测量值的显着改善。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号