- Tytuł:
- Feature selection using particle swarm optimization in text categorization
- Autorzy:
-
Aghdam, M. H.
Heidari, S. - Powiązania:
- https://bibliotekanauki.pl/articles/91792.pdf
- Data publikacji:
- 2015
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
classification system
feature selection
text categorization
particle swarm optimization (PSO)
system klasyfikacji
wybór funkcji
kategoryzacja tekstu
optymalizacja rojem cząstek - Opis:
- Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 231-238
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł