Towards the optimal feature selection in high-dimensional Bayesian network classifiers

Sammanfattning

We focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the growing dimension asymptotics. We modify the weighted BN by introducing inclusion-exclusion factors which eliminate the features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy

Publicerad i

Soft Methodology and Random Information Systems
2004, sidor: 613-620
Utgivare: Springer, Berlin

SLU författare

• von Rosen, Dietrich

  • Institutionen för energi och teknik, Sveriges lantbruksuniversitet
    

• Andrushchenko, Zhanna

  • Institutionen för energi och teknik, Sveriges lantbruksuniversitet
    

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/7268