Classification by likelihood accordance functions

Abstract

In this paper, we introduce the likelihood accordance function (LA function for short), which is defined to characterize the accordance of a new observation to be classified with training samples. The LA classifier is then constructed using the ratio of LA functions. It is shown that, the LA functions are invariant under orthogonal linear transformations, while LA classifier is invariant under non-degenerate linear transformations. Moreover, the asymptotic optimality of LA classifier is obtained. At last, several simulations illustrated that the new LA classifier performs much better than the traditional classifiers.

Keywords:

• Classification
• Likelihood accordance function
• LA classifier
• Asymptotic optimality

Acknowledgements

We would like to thank the reviewer for providing valuable and helpful comments.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant No. [11471035].