Instance Level Classification Confidence Estimation

Often the confidence of a classification prediction can be as important as the prediction itself although current classification confidence measures are not necessarily consistent between different data sets. Thus in this paper, we present an algorithm to predict instance level classification confidence that is more consistent between data sets and is intuitive to interpret. The results with five test cases show high correlation between true and predicted classification rate, i.e. the probability of assigning the correct class label, thus proving the validity of the proposed algorithm.