%0 Journal Article
%T On the Consistency of Multithreshold Entropy Linear Classifier
%A Czarnecki, Wojciech Marian
%J Schedae Informaticae
%V 2015
%R 10.4467/20838476SI.15.012.3034
%N Volume 24
%P 123-132
%K multithreshold classifier, entropy, consistency, classification theory
%@ 1732-3916
%D 2016
%U https://ejournals.eu/en/journal/schedae-informaticae/article/on-the-consistency-of-multithreshold-entropy-linear-classifier
%X 
	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.