Soft Confusion Matrix Classifier for Stream Classification

TL;DR

This paper introduces an improved soft confusion matrix classifier designed for stream learning, capable of incremental learning and effectively handling concept drift with reduced computational costs.

Contribution

It presents a wrapping-classifier that enables incremental learning for classifiers lacking this ability, incorporating a new fuzzy neighborhood definition and ADWIN-driven drift detection.

Findings

Significant performance improvement over reference methods

Effective handling of concept drift in stream data

Reduced computational costs of the SCM classifier

Abstract

In this paper, the issue of tailoring the soft confusion matrix (SCM) based classifier to deal with stream learning task is addressed. The main goal of the work is to develop a wrapping-classifier that allows incremental learning to classifiers that are unable to learn incrementally. The goal is achieved by making two improvements in the previously developed SCM classifier. The first one is aimed at reducing the computational cost of the SCM classifier. To do so, the definition of the fuzzy neighborhood of an object is changed. The second one is aimed at effective dealing with the concept drift. This is done by employing the ADWIN-driven concept drift detector that is not only used to detect the drift but also to control the size of the neighbourhood. The obtained experimental results show that the proposed approach significantly outperforms the reference methods.