On the Consistency of Multithreshold Entropy Linear Classifier

• Authors: Wojciech Marian Czarnecki
• Languages of publication: PL

Abstracts

PL

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.

Keywords

PL

multithreshold classifier, entropy, consistency, classification theory

• Publisher: [unknown2]
• Journal: Schedae Informaticae
• Year: 2015
• Volume: 24

Dates

published

2015

online

06 - 07 - 2016

Contributors

author

Wojciech Marian Czarnecki