On the consistency of Multithreshold Entropy Linear Classifier
This work provides theoretical validation for a recent classifier, which is incremental in nature.
The paper analyzes the consistency of the Multithreshold Entropy Linear Classifier (MELC) by showing that its objective function upper bounds misclassified points similarly to the hinge loss in support vector machines, with numerical experiments conducted on five datasets for confirmation.
Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea which employs information theoretic concept in order to create a multithreshold maximum margin model. In this paper we analyze its consistency over multithreshold linear models and show that its objective function upper bounds the amount of misclassified points in a similar manner like hinge loss does in support vector machines. For further confirmation we also conduct some numerical experiments on five datasets.