# Proposal and study of statistical features for string similarity computation and classification

arXiv: https://arxiv.org/abs/2605.15110 · HTML: https://mlsanity.com/paper/2605.15110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 5 of 5)

- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography](https://mlsanity.com/paper/2112.11381/) (2021)
- [Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier](https://mlsanity.com/paper/2112.12549/) (2021)
- [k-MS: A novel clustering algorithm based on morphological reconstruction](https://mlsanity.com/paper/2208.14390/) (2022)
- [Comparing Results of Thermographic Images Based Diagnosis for Breast Diseases](https://mlsanity.com/paper/2208.14410/) (2022)

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

- [Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset](https://mlsanity.com/paper/2605.23777/) (2026)

## Abstract (arXiv)

Adaptations of features commonly applied in the field of visual computing, co-occurrence matrix (COM) and run-length matrix (RLM), are proposed for the similarity computation of strings in general (words, phrases, codes and texts). The proposed features are not sensitive to language related information. These are purely statistical and can be used in any context with any language or grammatical structure. Other statistical measures that are commonly employed in the field such as longest common subsequence, maximal consecutive longest common subsequence, mutual information and edit distances are evaluated and compared. In the first synthetic set of experiments, the COM and RLM features outperform the remaining state-of-the-art statistical features. In 3 out of 4 cases, the RLM and COM features were statistically more significant than the second best group based on distances (P-value < 0.001). When it comes to a real text plagiarism dataset, the RLM features obtained the best results.