# Unbalanced sensitivity and specificity with high total accuracy in a binary classification case

I'm using MATLAB R2016a for binary classification (time series prediction) of a financial case. I have a good total accuracy (70~75%) but specificity is about 90% and sensitivity is about 60% and vice versa. Currently I'm using grid search to optimize my classification model (SVM, neural network, etc.) based on total accuracy. My data-set has balanced samples of binary output.

How can I improve results in this case? Can I use any other performance metric to take into account unbalanced sensitivity and specificity (as mentioned, in some cases I have higher specificity than sensitivity and in some cases vice versa.)?

• I'm not sure how to do this in Matlab, but in R I favor maximizing Kappa over Accuracy, especially when class frequencies are unbalanced. – HEITZ May 18 '16 at 23:36