A threshold varying bisection method for cost sensitive learning in neural networks
A threshold varying bisection method for cost sensitive learning in neural networks
复制标题
DOI：
10.1016/j.eswa.2007.01.011
复制
发表时间：
2005-12
期刊：
影响因子：
--
通讯作者：
P. Pendharkar
中科院分区：
文献类型：
--
作者：
P. Pendharkar
针对代价敏感神经网络的学习问题，提出了一种改变分类阈值的二分法。利用模拟数据和不同的误分类代价不对称性，对提出的阈值变化二分法进行了测试，并与传统的基于固定阈值的神经网络和概率神经网络进行了比较。实验结果表明，本文提出的变阈值分割方法比传统的基于固定阈值的神经网络方法具有更好的分割效果。然而，与概率神经网络相比，该方法只有在误分类代价不对称较低的情况下才有效。
We propose a bisection method for varying classification threshold value for cost sensitive neural network learning. Using simulated data and different misclassification cost asymmetries, we test the proposed threshold varying bisection method and compare it with the traditional fixed-threshold method based neural network and a probabilistic neural network. The results of our experiments illustrate that the proposed threshold varying bisection method performs better than the traditional fixed-threshold method based neural network. However, when compared to probabilistic neural network, the proposed method works well only when the misclassification cost asymmetries are low.