A threshold varying bisection method for cost sensitive learning in neural networks

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.