# Authorship Attribution Using the Chaos Game Representation

arXiv: https://arxiv.org/abs/1802.06007 · HTML: https://mlsanity.com/paper/1802.06007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Enhancing Authorship Attribution through Embedding Fusion: A Novel Approach with Masked and Encoder-Decoder Language Models](https://mlsanity.com/paper/2411.00411/) (2024)

## Abstract (arXiv)

The Chaos Game Representation, a method for creating images from nucleotide sequences, is modified to make images from chunks of text documents. Machine learning methods are then applied to train classifiers based on authorship. Experiments are conducted on several benchmark data sets in English, including the widely used Federalist Papers, and one in Portuguese. Validation results for the trained classifiers are competitive with the best methods in prior literature. The methodology is also successfully applied for text categorization with encouraging results. One classifier method is moreover seen to hold promise for the task of digital fingerprinting.