Abstract: Online writeprint identification is a technique to identify individuals based on textual identity cues people leave behind online messages. Character N-gram is one of the most effective approaches to identify writeprint according to previous research. "ho deal with the high dimensional and redundant feature problems and the property of each feature being valuable for the task of writeprint identification, an ensemble learning approach based on feature subspacing was proposed in this study. The essence of this method is to partition the features into distinct subsets. Firstly, the whole feature set is split into equally sized and disjoint subsets. Then each of them is used to train a base classifier using Multinomial Naive Bayes. Finally, these individual classifiers arc aggregated to construct the ensemble via an appropriate combination rule which is a simple average of arithmetic mean and geometric mean. Additionally, genetic algorithm was used to optimize the feature subspacing (i. e. feature subsets selection). To examine the approach, experiment was conducted on a real world test bed. Performance results showed the proposed approach was quite effective and obtained a considerable improvement in accuracy compared with the benchmark technique in writeprint identification (Support Vector Machine).

Key words: Writeprint identification, Ensemble learning, Genetic algorithm, Feature subset