blind label ratio estimation

Many anomaly detection algorithms require knowledge of the ratio of the two labels to operate‎. ‎in real life‎, ‎however‎, ‎we may not have access to this value‎. ‎as such‎, ‎we often run anomaly detection packages with default values that may differ significantly from the actual value‎. ‎experiments on multiple datasets show that correctly determination of this ratio or at least obtaining a close estimate can makes a significant difference in the final performance of the anomaly detection algorithm‎. ‎in this paper‎, ‎we address the problem of estimating this ratio using both theoretical and heuristic techniques‎. ‎in the theoretical method‎, ‎we maximize the mutual information between features and labels to find the exact ratio‎. ‎in the heuristic method‎, ‎we sweep the [0,1] range in 0.01 steps to search for the ratio‎. ‎on each iteration‎, ‎we run the anomaly detection algorithm based on the ratio for that iteration and record the correlation coefficient between the features and the label generated by the algorithm‎. ‎after the 100th iteration‎, ‎we declare the ratio that provides the maximum correlation coefficient as our estimate of the label ratio‎. ‎our experiments on multiple datasets and several anomaly detection algorithms show that maximizing the correlation coefficient leads to the best results.