A discriminative random sampling strategy with individual-author feature selection for writeprint recognition of Chinese texts

Abstract

The auto authorship recognition has become a novel technique to investigate cybercrimes. But the challenge of the research is that a huge number of features exist in the moderate-sized corpus, which causes the curse of over-training. Besides, it is hard to distinguish between potential authors only by a single feature set. In this paper, we proposed a random sampling style ensemble method with individual-author feature selection to exploit the high-dimensional feature space. The proposed method randomly picks writing-style features on each individual-author feature set (IAFS) partitioned from the whole feature set. The IAFSs are heuristically selected with training set of each author. Then, multiple base classifiers (BCs) are formed on the sampled feature sets. Finally, all BCs are fused to get a final decision. Experimental results on the real-life Chinese forum data verify the robustness of the proposed method compared with conventional ensemble methods. We also analyze the diversity of algorithm to reveal that the ensemble strategy is more effective and can construct more diverse BCs than random subspace methods.

Keywords:

• Writeprint recognition
• individual-author feature set (IAFS)
• random subspace method (RSM)
• class separability measure
• diversity

Acknowledgments

The authors sincerely thank anonymous reviewers for their constructive comments, which helped improve this paper.