Classifying Signals with Local Classifiers

TL;DR

This paper introduces a novel classification method for signals that combines the lifting scheme with support vector machines to create interpretable and effective local classifiers, demonstrated on artificial and real datasets.

Contribution

The paper presents a new approach for building local classifiers using a combination of the lifting scheme and support vector machines, enhancing interpretability and effectiveness.

Findings

Effective classification on artificial datasets

Successful application to real-world signals

Improved interpretability of classifiers

Abstract

This paper deals with the problem of classifying signals. The new method for building so called local classifiers and local features is presented. The method is a combination of the lifting scheme and the support vector machines. Its main aim is to produce effective and yet comprehensible classifiers that would help in understanding processes hidden behind classified signals. To illustrate the method we present the results obtained on an artificial and a real dataset.