TY - JOUR
T1 - Plagiarism Detection with Genetic-Based Parameter Tuning
AU - Sanchez-Perez, Miguel A.
AU - Gelbukh, Alexander
AU - Sidorov, Grigori
AU - Gómez-Adorno, Helena
N1 - Publisher Copyright:
© 2018 World Scientific Publishing Company.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Plagiarism detection
KW - genetic algorithms
KW - optimization
KW - text alignment
UR - http://www.scopus.com/inward/record.url?scp=85028295384&partnerID=8YFLogxK
U2 - 10.1142/S0218001418600066
DO - 10.1142/S0218001418600066
M3 - Artículo
SN - 0218-0014
VL - 32
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
IS - 1
M1 - 1860006
ER -