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Author identification algorithms attempt to ascribe document to author, with an eye towards diverse application areas including: forensic evidence, authenticating communications, and intelligence gathering. We view author identification as a single label classification problem, where 2000 authors would imply 2000 possible categories to assign to a post. Experiments with a naive Bayes classifier on a blog author identification task demonstrate a remarkable tendency to over-predict the most prolific authors. Literature search confirms that the class imbalance phenomenon is a challenge for author identification as well as other machine learning tasks. We develop a vector projection method to remove this hazard, and achieve a 63% improvement in accuracy over the baseline on the same task. Our method adds no additional asymptotic computational complexity to naive Bayes, and has no free parameters to set. The …