Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification.

Xing, Ying; Lin, Wanting; Lin, Xueyan; Yang, Bin; Tan, Zhou

Xing, Ying; Lin, Wanting; Lin, Xueyan; Yang, Bin; Tan, Zhou.

Afiliación

Xing Y; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, 100876 Beijing, China.
Lin W; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, 100876 Beijing, China.
Lin X; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, 100876 Beijing, China.
Yang B; Du Xiaoman (Beijing) Science Technology Co., Ltd., 100000 Beijing, China.
Tan Z; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, 100876 Beijing, China.

Comput Intell Neurosci ; 2022: 2320447, 2022.

Article en En | MEDLINE | ID: mdl-35479605

Asunto(s)

Inteligencia Artificial; Programas Informáticos; Bases de Datos Factuales

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Inteligencia Artificial Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Intell Neurosci Asunto de la revista: INFORMATICA MEDICA / NEUROLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google