Hybrid classification with partial models

Document Type

Conference Proceeding

Date of Original Version



The parametric classifiers trained with the Bayesian rule are usually more accurate than the non-parametric classifiers such as nearest neighbors, neural network and support vector machine, when the class-conditional densities of distribution models are known except for some of their parameters and the training data is abundant. However, the parametric classifiers would perform poorly if these class-conditional densities are unknown and the assumed distribution models are inaccurate. In this paper, we propose a hybrid classification method for the data with partially known distribution models where only the distribution models of some classes are known. For this partial models case, the proposed hybrid classifier makes the best use of knowledge of known distribution models with Bayesian interference, while both purely parametric and non-parametric classifiers would lose a specific predictive capacity for classification. Theoretical proofs and experimental results show that the proposed hybrid classifier has much better performance than these purely parametric and non-parametric classifiers for the data with partial models.

Publication Title

Proceedings of the International Joint Conference on Neural Networks