Most of the techniques used in text classification are determined by the occurrences of the words (terms) appearing in the documents, combined with the user feedback over the documents retrieved. However, in our model, the most relevant terms will be selected from a previous fuzzy classification given by the genetic algorithm guided by the user feedback, but using techniques from Machine Learning. A feature selection process is carried out through a Genetic Algorithm in order to find the most discriminatory terms to be stored as the user profile.
@article{urn:eudml:doc:39205,
title = {Using genetic feature selection for optimizing user profiles.},
journal = {Mathware and Soft Computing},
volume = {7},
year = {2000},
pages = {275-286},
zbl = {0995.68604},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39205}
}
Larsen, Henrik Legind; Marín, Nicolás; Martín-Bautista, María José; Vila, M. Amparo. Using genetic feature selection for optimizing user profiles.. Mathware and Soft Computing, Tome 7 (2000) pp. 275-286. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39205/