I have a data set with highly positively correlated features that I'm classifying with LR. AFAIK correlated weights are not a problem in the same way they are in Naive Bayes - overcounting will not occur with LR.
The strange things that I'm seeing is that some of the highly correlated features assume opposite weights: feature A might be highly positive and feature B highly negative, though not as much. Is this a symptom of something going wrong with optimization or is this expected (a priori I expect A and B to be positive class indicators)