Feature Bagging for Author Attribution

The authorship attribution literature demonstrates the difficulty to design classifiers overcoming simple strategies such as linear classifiers operating on a number, most frequent, of lexical features such as character trigrams. We claim this comes, at least partially, from the difficulty to efficiently learn the contribution of all features, which leads to either undertraining or overtraining of classifiers. To overcome this difficulty we propose to use bagging techniques that rely on learning classifiers on different random subset of features, then to combine their decision by making them vote.