| Revista: | Journal of applied research and technology |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000382992 |
| ISSN: | 1665-6423 |
| Autores: | Naeem, M1 Asghar, S2 |
| Instituciones: | 1University Islamabad, Faculty of Computing Mohammad Ali Jinnah, Islamabad. Pakistán 2COMSATS Institute of Information Technology, Islamabad. Pakistán |
| Año: | 2014 |
| Periodo: | Ago |
| Volumen: | 12 |
| Número: | 4 |
| Paginación: | 734-749 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | Bayesian Belief Network (BBN) is an appealing classification model for learning causal and noncausal dependencies among a set of query variables. It is a challenging task to learning BBN structure from observational data because of pool of large number of candidate network structures. In this study, we have addressed the issue of goodness of data fitting versus model complexity. While doing so, we have proposed discriminant function which is non-parametric, free of implicit assumptions but delivering better classification accuracy in structure learning. The contribution in this study is twofold, first contribution (discriminant function) is in BBN structure learning and second contribution is for Decision Stump classifier. While designing the novel discriminant function, we analyzed the underlying relationship between the characteristics of data and accuracy of decision stump classifier. We introduced a meta characteristic measure AMfDS (herein known as Affinity Metric for Decision Stump) which is quite useful in prediction of classification accuracy of Decision Stump. AMfDS requires a single scan of the dataset |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial, Aprendizaje de máquinas, Redes bayesianas, Ajuste de datos, Arboles de decisión |
| Keyword: | Computer science, Artificial intelligence, Machine learning, Bayesian networks, Data fitting, Decision trees |
| Texto completo: | Texto completo (Ver HTML) |
