A multistrategy approach for digital text categorization.

Castillo, María Dolores, and Serrano, José Ignacio. "A multistrategy approach for digital text categorization.." Mathware and Soft Computing 12.1 (2005): 15-32. <http://eudml.org/doc/40854>.

@article{Castillo2005,
abstract = {The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents, the system combines the predictions of the learners by applying evolutionary techniques as well. The system relies on a modular , flexible architecture that makes no assumptions about the design of learners or the number of learners available and guarantees the independence of the thematic domain.},
author = {Castillo, María Dolores, Serrano, José Ignacio},
journal = {Mathware and Soft Computing},
keywords = {Inteligencia artificial; Aprendizaje; Algoritmos de clasificación; Algoritmos genéticos},
language = {eng},
number = {1},
pages = {15-32},
title = {A multistrategy approach for digital text categorization.},
url = {http://eudml.org/doc/40854},
volume = {12},
year = {2005},
}

TY - JOUR
AU - Castillo, María Dolores
AU - Serrano, José Ignacio
TI - A multistrategy approach for digital text categorization.
JO - Mathware and Soft Computing
PY - 2005
VL - 12
IS - 1
SP - 15
EP - 32
AB - The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents, the system combines the predictions of the learners by applying evolutionary techniques as well. The system relies on a modular , flexible architecture that makes no assumptions about the design of learners or the number of learners available and guarantees the independence of the thematic domain.
LA - eng
KW - Inteligencia artificial; Aprendizaje; Algoritmos de clasificación; Algoritmos genéticos
UR - http://eudml.org/doc/40854
ER -