Título

Symbolic one-class learning from imbalanced datasets: Application in medical diagnosis

Autor

LUIS JAVIER MENA CAMARE

JESUS ANTONIO GONZALEZ BERNAL

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Materias

Machine learning - (MACHINE LEARNING) Imbalanced datasets - (IMBALANCED DATASETS) One-class learning - (ONE-CLASS LEARNING) Classification algorithm - (CLASSIFICATION ALGORITHM) Rule extraction - (RULE EXTRACTION) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)

Resumen o descripción

When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make an overview of the class imbalance problem; we review consequences, possible causes and existing strategies to cope with the inconveniences associated to this problem. As an effort to contribute to the solution of this problem, we propose a new rule induction algorithm named Rule Extraction for MEdical Diagnosis (REMED), as a symbolic one-class learning approach. For the evaluation of the proposed method, we use different medical diagnosis datasets taking into account quantitative metrics, comprehensibility, and reliability. We performed a comparison of REMED versus C4.5 and RIPPER combined with over-sampling and cost-sensitive strategies. This empirical analysis of the REMED algorithm showed it to be quantitatively competitive with C4.5 and RIPPER in terms of the area under the Receiver Operating Characteristic curve (AUC) and the geometric mean, but overcame them in terms of comprehensibility and reliability. Results of our experiments show that REMED generated rules systems with a larger degree of abstraction and patterns closer to well-known abnormal values associated to each considered medical dataset.

Editor

World Scientic Publishing Company

Fecha de publicación

2009

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Recurso de información

http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1181

Formato

application/pdf

Idioma

Inglés

Relación

&

Gonzalez-Bernal, J.A. (2009). Symbolic one-class learning from imbalanced datasets: Application in medical diagnosis, International Journal on Articial Intelligence Tools, Vol. 18 (2): 273-309

Audiencia

Estudiantes

Investigadores

Público en general

Sugerencia de citación

Mena-Camare, L.

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

340