Name
Abstract | ||
|---|---|---|
A crucial step in plagiarism detection is text alignment. This task consists in finding similar text fragments between two given documents. We introduce an optimization methodology based on genetic algorithms to improve the performance of a plagiarism detection model by optimizing its input parameters. The implementation of the genetic algorithm is based on nonbinary representation of individuals, elitism selection, uniform crossover, and high mutation rate. The obtained parameter settings allow the plagiarism detection model to achieve better results than the state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0218001418600066 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Plagiarism detection, text alignment, genetic algorithms, optimization | Data mining,Crossover,Mutation rate,Plagiarism detection,Computer science,Artificial intelligence,Text alignment,Machine learning,Genetic algorithm | Journal |
Volume | Issue | ISSN |
32 | 1 | 0218-0014 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel A. Sanchez-Perez | 1 | 15 | 2.81 |
| Alexander Gelbukh | 2 | 2843 | 269.19 |
| Grigori Sidorov | 3 | 398 | 60.51 |
| Helena Gómez-Adorno | 4 | 40 | 16.01 |